Submitted:
06 September 2023
Posted:
11 September 2023
You are already at the latest version
Abstract

Keywords:
1. Introduction
2. Materials and Methods
2.1. Survey
2.2. Classification of emotions
- Preprocessing: any given input text was preprocessed by removing irrelevant information, text was converted to lowercase, words were tokenized, and stop words and punctuation were removed.
- Prompt formulation: a prompt was formulated in order to include the preprocessed input text and the model was asked to identify the emotion(s) associated with it. The prompt explicitly listed the eight target emotions (anger, anticipation, joy, trust, fear, surprise, sadness, and disgust) and the model was instructed to select the most appropriate one(s). In particular, we used the Google Sheet plugin “GPT for Sheets” by Talarian (https://gptforwork.com/), with the following prompt: “Perform sentiment analysis and input 1 or 0 separated by commas for each of the following emotions: anger, anticipation, joy, trust, fear, surprise, sadness and disgust. Example: if all are present 1,1,1,1,1,1,1,1; if all are absent 0,0,0,0,0,0,0,0; if only anger is present 1,0,0,0,0,0,0,0, etc…”
- Inference: GPT-3.5-turbo generated an output based on its pre-trained knowledge and the context provided by the prompt for each one of the 1136 rows. The output was stored as a string of comma-separated binary values in two Google sheet cells, one for ‘pre-covid emotions’, one for ‘post-covid emotions’.
- Post-processing: the Google sheet document was then imported into StataBE 18 (StataCorp) and the emotion strings were converted in regular fields for each emotion coded as absent (0) or present (1), both before and after pandemic.
2.3. Classification of themes
- Tokenization. Tokenization is the process of segmenting the input text into individual units called tokens, which can be words, phrases, or punctuation marks. This is an essential pre-processing step in NLP. The GPT model tokenized the text to facilitate further analysis, as it allowed for easier identification and quantification of the most recurring words and phrases. This was done using the model's internal tokenization algorithms, which have been trained to recognize and separate the constituent elements of different languages, including Italian.
- Frequency Analysis. After tokenization, the GPT model conducted a frequency analysis of the words and phrases in the text. This involved counting the occurrences of each token and ranking them based on their frequency. This step was crucial for determining the prominence and significance of particular words and phrases in the text, which helped in identifying the main themes. The frequency analysis was performed by leveraging the model's internal algorithms for counting and ranking tokens.
- Semantic Analysis. In order to group related words and phrases into overarching themes, the GPT model performed semantic analysis. This involved leveraging its pretrained knowledge of language and understanding of semantics to identify words and phrases with similar meanings or connotations. The model used its internal algorithms to analyze the relationships between tokens, taking into account their context and meaning, and grouped them into themes that represented the broader concepts present in the text.
- Scoring. The GPT model assigned a score to each theme based on the frequency of the words and phrases within the theme. Once the text was analyzed to identify words and phrases that were indicative of the themes in question, words were counted, and the proportion of words associated with each theme was calculated as a percentage of the total number of words in the text. This percentage was then mapped to a scale of 0 to 10, where a score of 0 represented no presence of the theme in the text, and a score of 10 represented extremely high prominence of the theme. This scoring system allowed for a relative comparison of the prominence of different themes within the text, based on the frequency and distribution of indicative words and phrases.
- Textual Citations. To provide evidence for each identified theme, the GPT model extracted relevant textual citations in Italian from the original text. This process involved identifying sentences or phrases that exemplified the main ideas of each theme, while ensuring that the citations were representative of the broader context in which the theme appeared. The model leveraged its understanding of language and context to select appropriate citations that effectively illustrated the themes and their significance within the text. Citations were manually checked for accuracy and translated into English by the authors.
2.4. Thematic map
3. Results
3.1. Characteristics of the sample
3.2. Emotions related to birth
3.3. Communication
3.3.1. HCPs and communication
- Fear and Anxiety (Score 8/10). Fear and anxiety were the most common emotions expressed throughout the text. Women reported feeling scared, uncertain, and panicked due to the pandemic and the information provided by healthcare professionals. This theme was prevalent across all the text. Example of words used: "Fear, Anxiety, Panic, Worry, Apprehension, Tension, Dread, Terror, Anguish, Insecurity".
- Uncertainty and Confusion (Score 7/10). Many respondents described the communication from healthcare professionals as unclear, contradictory, or insufficient, leading to feelings of confusion and uncertainty. This theme was consistently present in the text. Example of words used: "Uncertainty, Confusion, Disorientation, Doubt, Insecurity, Vagueness, Ambiguity, Disinformation, Hesitation, Contradictions".
- Emotional Support and Reassurance (Score 6/10). Some respondents mentioned the importance of emotional support and reassurance provided by healthcare professionals. Women reported that clear, direct, comforting and empathetic communication from their HCPs helped foster a sense of hope, even in the face of uncertainty and fear. Seeking support from HCPs was used as a coping strategy which enabled women to focus on the positive aspects of their care. Example of words used: "Emotional Support, Reassurance, Comfort, Calmness, Serenity, Hope, Encouragement, Understanding, Empathy, Positivity".
- Professionalism and Competence (Score 5/10). Several individuals highlighted the professionalism and competence of healthcare workers during the pandemic. This theme recurred across the text, emphasizing the importance of skilled professionals in handling the crisis. Example of words used: "Professionalism, Competence, Expertise, Prepared, Dedication, Commitment, Excellence, Skilled, Efficient, Focused, Humanity".
- Distance and Detachment (Score 4/10). The text also reflected a sense of distance and detachment, both in terms of the physical limitations imposed by the pandemic and the emotional disconnection experienced by some women in their interactions with healthcare professionals. Example of words used: "Distance, Detachment, Isolation, Separation, Limited contact, Disconnection, Remote, No feeling”.
3.3.2. Media and communication
- Fear and Anxiety (Score: 10/10). The most prominent theme in the text is the sense of panic and anxiety induced by media communication during the pandemic. Respondents frequently mentioned feeling overwhelmed by the constant bombardment of alarming and sensationalist news. They reported feelings of fear, unease, and insecurity, which negatively impacted their mental health and well-being. Example of words used: "Panic, Anxiety, Terror, Fear, Insecurity, Overwhelmed, Alarming, Sensationalist news".
- Confusion and Contradictions (Score: 8/10). Another significant theme is the confusion and contradictions in the media's messaging. Participants expressed frustration with the lack of clarity and consistency in the information provided, which led to uncertainty and doubt about the situation. Example of words used: “Confusion, Contradictions, Inconsistencies, Mixed messages, Unclear guidance, Ambiguity, Discrepancies, Misinformation, Divergent opinions, Lack of clarity”.
- Sensationalism and Alarmism (Score: 7/10). Many respondents criticized the media's sensationalist and alarmist approach, which exacerbated feelings of panic and anxiety. They accused the media of exaggerating the situation, focusing on negative aspects, and employing a fear-based narrative to capture attention. Many women perceive a disproportionate emphasis on extreme cases, dire projections, and tragic stories, while neglecting to provide context or balance with more positive or hopeful information. Such reporting practices were perceived as profit-driven, prioritizing viewer engagement and clickbait headlines over accurate and responsible journalism. As a coping strategy, women tried to limit exposure to distressing news, seeking out positive news stories. Example of words used: “Sensationalism, Alarmism, Exaggeration, Hype, Fearmongering, Over-dramatization, Catastrophizing, Scaremongering, Apocalyptic language, Inflated claims”.
- Misinformation and Inaccuracy (Score: 6/10). The final theme we identified is the prevalence of misinformation and inaccuracy in media reporting. Participants mentioned issues with fake news, untrustworthy sources, and imprecise information that contributed to a chaotic and disorienting information landscape. Example of words used: “Misinformation, Inaccuracy, False claims, Unverified reports, Disinformation, Rumors, Distorted facts, Misleading statements, Unreliable sources, Fabricated stories".
3.3.3. Peers and communication
- Remote Communication (Score: 8.5/10). The most recurrent theme was the reliance on remote communication methods, including phone calls, video calls, and messaging apps, as face-to-face interactions were limited. Participants often mentioned using these technologies to maintain social connections and exchange support during the pandemic. Example of words used: “Video Calls, Phone Calls, Messaging Apps, Online Platforms, Virtual Meetings, Text Messages, Social Media, Email”.
- Adaptation and Coping Strategies (Score: 7.5/10). Adjusting to the new normal and coping strategies emerged as a main theme in participants' descriptions of their experiences during the pandemic. Among the most cited strategies were seeking emotional support from family, friends, and colleagues, as well as engaging in activities to distract from stress and uncertainty. Virtual connections through video calls and social media proved invaluable for maintaining relationships and alleviating feelings of isolation. Additionally, adopting positive mindsets, focusing on self-care, and staying informed were recurring coping techniques. Example of words used: “Adaptation, Modifying, Coping, Reinventing, Emotional Support, Distraction, Virtual Connection, Positivity, Helping Others”.
- Emotional Support (Score: 7/10). Participants frequently discussed the importance of providing and receiving emotional support during this challenging period. Sharing feelings, thoughts, and concerns with their peers was a way to alleviate stress and anxiety related to the pandemic. Example of words used: “Empathy, Comfort, Reassurance, Kindness, Encouragement, Caring, Understanding, Compassion, Love, Listening”.
- Information Sharing (Score: 6/10). Another significant theme was the exchange of information about the pandemic, such as updates on restrictions, health guidelines, and the availability of essential resources. Participants often highlighted how sharing information with their peers helped them stay informed and cope with the rapidly changing situation. Example of words used: "Updates, News, Facts, Details, Guidelines, Recommendations, Protocols, Procedures, Instructions, Advice".
- Adaptation to the New Normal (Score: 5.5/10). Participants also mentioned adjusting to the new normal, as the pandemic forced them to modify their communication habits and routines. This theme covered the adoption of new technologies, the struggle to maintain relationships, and the development of new ways of socializing. Example of words used: “Adjusting, Adapting, Modifying, Flexibility, Changing, Coping, Reinventing, Reshaping, Reimagining, Transforming”.
- Fear and Anxiety (Score: 5.5./10). Although less prevalent than in the other sections, the theme of fear and anxiety is present also in the peers section, particularly related to the risk of infection from social interactions. Women expressed concerns about their peers' and family members' adherence to safety guidelines, which might expose them and their children to the virus. Example of words used: "Scared, Worried, Anxious, Stressed, Concerned, Nervous".
3.3.4. Open-end questions
- Fear and anxiety (Score: 9/10). Again, this theme was the most prominent in the text, as many women express their fear and anxiety about various aspects of their situation, such as getting infected, losing their loved ones, being alone during labor, facing economic difficulties, and not knowing when the emergency will end. Some examples of textual quotations are: "I think about suicide rather than going to the hospital and risking contracting covid", "I'm afraid I won't be able to protect my little one", "I'm scared because my childbirth would take place in less than 2 months and here we are talking about a virus that we don't know much about", "I'm afraid that life will never go back to how it was before".
- Loneliness and isolation (Score: 8/10). This theme is also very frequent in the text, as many women miss the lack of social contact and support from their family and friends, especially during a delicate moment like pregnancy or breastfeeding. Some examples of textual quotations are: "I feel emotionally alone, I have no one, apart from my partner, with whom to share anxieties, fears, but also joys and progress", "I miss my family and I'm sorry that they are losing the first months of my baby", "I miss being able to see my parents and take my son to the park".
- Gratitude and hope (Score: 6/10). This theme is less frequent but still present in the text, as some women express their gratitude and hope for their situation, such as having a healthy baby, a supportive partner, or express hope for the future. Some examples of textual quotations are: "I feel lucky because, unlike other colleagues, I can stay at home and not risk it", "I'm grateful to have a baby that makes us happy, me and my husband, and fills our little house with love", "I hope that at least some women will understand how sick the world is because of globalization / pollution , too much work and how important it is to have friends and family".
- Disappointment and sadness (Score: 5/10). This theme is also present in the text, as some women express their disappointment and sadness for not being able to live their pregnancy or breastfeeding as they had imagined or planned, due to the restrictions and limitations imposed by the pandemic. Some examples of textual quotations are: "I wish I could live this pregnancy differently....especially sharing it closely with all the women who are important to me", "I'm sad because no relative has been able to meet my son", "The fear takes away from me the enthusiasm of pregnancy", "I miss being able to go swimming and go out with my family".
4. Discussion
4.1. Emotions regarding childbirth
4.2. Communication and open-ending questions: the main emerging themes
4.3. Strengths and limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Remuzzi, A. and Remuzzi, G. COVID-19 and Italy: what next? Lancet 2020, 395, 1225–1228. [Google Scholar] [CrossRef] [PubMed]
- Rossi, R. et al. COVID-19 Pandemic and Lockdown Measures Impact on Mental Health Among the General Population in Italy. Front Psychiatry 2020, 11, 790. [Google Scholar] [CrossRef] [PubMed]
- Carpiniello, B. and Vita, A. Impact of COVID-19 on the Italian Mental Health System: A Narrative Review. Schizophr Bull Open 2022, 3, sgac038. [Google Scholar] [CrossRef] [PubMed]
- Lu, X. and Lin, Z. COVID-19, Economic Impact, Mental Health, and Coping Behaviors: A Conceptual Framework and Future Research Directions. Front Psychology 2021, 12, 759974. [Google Scholar] [CrossRef] [PubMed]
- Loughnan, S.A. et al. Multicountry study protocol of COCOON: COntinuing Care in COVID-19 Outbreak global survey of New, expectant, and bereaved parent experiences. BMJ Open 2022, 12, e061550. [Google Scholar] [CrossRef] [PubMed]
- Filippetti, M.L. , Clarke, A.D.F. and Rigato, S. The mental health crisis of expectant women in the UK: effects of the COVID-19 pandemic on prenatal mental health, antenatal attachment and social support. BMC Pregnancy Childbirth 2022, 22, 68. [Google Scholar] [CrossRef]
- Ahmad, M. and Vismara, L. The Psychological Impact of COVID-19 Pandemic on Women’s Mental Health during Pregnancy: A Rapid Evidence Review. Int J Environ Res Public Health 2021, 18, 7112–7112. [Google Scholar] [CrossRef]
- Salehi, L. , Rahimzadeh, M., Molaei, E., Zaheri, H. and Saeieh, S.E. The relationship among fear and anxiety of COVID-19, pregnancy experience, and mental health disorder in pregnant women: A structural equation model. Brain Behav 2020, 10, e01835. [Google Scholar] [CrossRef]
- White, T. , Matthey, S., Boyd, K. and Barnett, B. Postnatal depression and post-traumatic stress after childbirth: Prevalence, course and co-occurrence. J Reprod Infant Psychology 2006, 24, 107–120. [Google Scholar] [CrossRef]
- Czarnocka, J. and Slade, P. Prevalence and predictors of post-traumatic stress symptoms following childbirth. Brit J Clin Psychol 2000, 39, 35–51. [Google Scholar] [CrossRef]
- Mental Health and Substance Use. Available online: https://www.who.int/teams/mental-health-and-substance-use/promotion-prevention/maternal-mental-health (accessed on 27 June 2023).
- Khoury, J.E. , Atkinson, L., Bennett, T., Jack, S.M. and Gonzalez, A. Coping strategies mediate the associations between COVID-19 experiences and mental health outcomes in pregnancy. Arch Womens Ment Health 2021, 24, 1007–1017. [Google Scholar] [CrossRef] [PubMed]
- Khoury, J.E. , Atkinson, L., Bennett, T., Jack, S.M. and Gonzalez, A. COVID-19 and mental health during pregnancy: The importance of cognitive appraisal and social support. J Affect Disorders 2021, 282, 1161–1169. [Google Scholar] [CrossRef] [PubMed]
- Ravaldi, C., Wilson, A., Ricca, V., Homer, C. and Vannacci, A. Pregnant women voice their concerns and birth expectations during the COVID-19 pandemic in Italy. Women Birth 2020, 335-343.
- Ravaldi, C. , Ricca, V., Wilson, A., Homer, C. and Vannacci, A. Previous psychopathology predicted severe COVID-19 concern, anxiety, and PTSD symptoms in pregnant women during ‘lockdown’ in Italy. Arch Womens Ment Health 2020, 23, 783–786. [Google Scholar] [CrossRef] [PubMed]
- Ravaldi, C. et al. Exclusive breastfeeding and women’s psychological well-being during the first wave of COVID-19 pandemic in Italy. Front Public Health 2022, 10, 965306. 2022; 10.
- Ravaldi, C. and Vannacci, A. The COVID-ASSESS dataset - COVID19 related anxiety and stress in prEgnancy, poSt-partum and breaStfeeding during lockdown in Italy. Data brief 2020, 33, 106440–6. [Google Scholar] [CrossRef] [PubMed]
- Ravaldi, C. and Vannacci, A. COVID-ASSESS Italy - COVID19 related Anxiety and StreSs in prEgnancy, poSt-partum and breaStfeeding. Mendeley 2020, V1.
- Introducing ChatGPT. Available online: https://openai.com/blog/chatgpt (accessed on 22 June 2023).
- Wu, T. et al. A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development. IEEE/CAA Autom Sinica 2023, 10, 1122–1136. [Google Scholar] [CrossRef]
- GPT-4. Available online: https://openai.com/gpt-4 (accessed on 22 June 2023).
- Bell, A.F. and Andersson, E. The birth experience and women’s postnatal depression: A systematic review. Midwifery 2016, 39, 112–123. [Google Scholar] [CrossRef]
- Henriksen, L., Grimsrud, E., Schei, B. and Lukasse, M. Factors related to a negative birth experience – A mixed methods study. Midwifery 2017, 51, 33–39.
- Smorti, M., Gemignani, A., Bonassi, L., Mauri, G., Carducci, A. and Ionio, C. The impact of Covid-19 restrictions on depressive symptoms in low-risk and high-risk pregnant women: a cross-sectional study before and during pandemic. BMC Pregnancy Childb 2022, 22, 191. [CrossRef]
- Azoulay, E. et al. Symptoms of Mental Health Disorders in Critical Care Physicians Facing the Second COVID-19 Wave: A Cross-Sectional Study. Chest 2021, 160, 944–955. [Google Scholar] [CrossRef]
- Infodemic. Available online: https://www.who.int/health-topics/infodemic (accessed on 25 July 2023).
- Sasaki, N. , Kuroda, R., Tsuno, K. and Kawakami, N. Exposure to media and fear and worry about COVID-19. Psychiatry Clin Neurosci 2020, 74, 501–502. [Google Scholar] [CrossRef]
- Koffman, J. , Gross, J., Etkind, S.N. and Selman, L. Uncertainty and COVID-19: how are we to respond? J R Soc Med 2020, 113, 211–216. [Google Scholar] [CrossRef]
- Ruiu, M.L. Mismanagement of Covid-19: lessons learned from Italy. Journal of Risk Research 2020, 23, 1007–1020. [Google Scholar] [CrossRef]
- Dimino, K. , Horan, K.M. and Stephenson, C. Leading Our Frontline HEROES Through Times of Crisis With a Sense of Hope, Efficacy, Resilience, and Optimism. Nurse Lead 2020, 18, 592–596. [Google Scholar] [CrossRef] [PubMed]
- Webb, R. et al. When birth is not as expected: a systematic review of the impact of a mismatch between expectations and experiences. BMC Pregnancy Childb 2021, 21, 475. [Google Scholar] [CrossRef] [PubMed]
- Xiang, Y.T. et al. Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. Lancet Psychiatry 2020, 7, 228–229. [Google Scholar] [CrossRef]
- Usher, K., Bhullar, N. and Jackson, D. Life in the pandemic: Social isolation and mental health. J Clin Nurs 2020, 29, 2756–2757. [CrossRef] [PubMed]
- Brooks, S.K. et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet 2020, 395, 912–920. [CrossRef]
- Niewiadomska, I., Bień, A., Rzońca, E. and Jurek, K. The Mediating Role of Dispositional Optimism in the Relationship between Health Locus of Control and Self-Efficacy in Pregnant Women at Risk of Preterm Delivery. Int J Environ Res Public Health 2022, 19, 6075. [CrossRef]
- Jiang, F. et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017, 2, 230–243. [CrossRef]
- Alm, C.O. and Hedges, A. Visualizing NLP in Undergraduate Students’ Learning about Natural Language. AAAI 2021, 35, 15480–15488. [Google Scholar] [CrossRef]
- Deng, J. and Lin, Y. The Benefits and Challenges of ChatGPT: An Overview. FCIS 2023, 2, 81–83. [Google Scholar] [CrossRef]



| Group | ||||||||
| Pregnant | Post-partum | Total | χ2 | p | ||||
| No. | % | No. | % | No. | % | |||
| Age Classes | ||||||||
| 18-25 | 15 | 1.3% | 8 | 1.3% | 23 | 1.3% | 3.075 | 0.380 |
| 25-30 | 157 | 13.8% | 74 | 11.6% | 231 | 13.0% | ||
| 30-35 | 436 | 38.4% | 268 | 42.0% | 704 | 39.7% | ||
| >35 | 528 | 46.5% | 288 | 45.1% | 816 | 46.0% | ||
| Level of education | ||||||||
| Lower secondary education | 38 | 3.4% | 25 | 4.0% | 63 | 3.5% | 5.884 | 0.436 |
| Upper secondary education | 291 | 25.6% | 180 | 28.2% | 471 | 26.6% | ||
| Post-secondary non-tertiary education | 289 | 25.4% | 148 | 23.2% | 437 | 24.6% | ||
| First stage of tertiary education | 322 | 28.3% | 194 | 30.4% | 516 | 29.1% | ||
| Second stage of tertiary education | 196 | 17.3% | 91 | 14.3% | 287 | 16.2% | ||
| Trimester (pregnant only) | ||||||||
| First | 113 | 9.9% | ||||||
| Second | 479 | 42.2% | ||||||
| Third | 544 | 47.9% | ||||||
| Baby age (months, post-partum only) | ||||||||
| < 1.4 | 243 | 38.1% | ||||||
| 1.5 - 3.9 | 194 | 30.4% | ||||||
| > 4 | 201 | 31.5% | ||||||
| Number of previous pregnancies | ||||||||
| Multigravidae | 745 | 65.6% | 394 | 61.8% | 1,139 | 64.2% | 2.602 | 0.107 |
| First pregnancy | 391 | 34.4% | 244 | 38.2% | 635 | 35.8% | ||
| Personal history | ||||||||
| Psychopathological History | 499 | 43.9% | 291 | 45.6% | 790 | 44.5% | 0.470 | 0.493 |
| Other children (n) | ||||||||
| 0 | 565 | 49.7% | 341 | 53.4% | 906 | 51.1% | 3.493 | 0.322 |
| 1 | 500 | 44.0% | 252 | 39.5% | 752 | 42.4% | ||
| 2 | 56 | 4.9% | 36 | 5.6% | 92 | 5.2% | ||
| >2 | 15 | 1.3% | 9 | 1.4% | 24 | 1.4% | ||
| Previous pregnancy loss | ||||||||
| No previous loss | 692 | 60.9% | 407 | 63.8% | 1,099 | 62.0% | 1.435 | 0.231 |
| Previous loss | 444 | 39.1% | 231 | 36.2% | 675 | 38.0% | ||
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
