Emotional reactions to infertility diagnosis: thematic and natural language processing analyses of the 1000 Dreams survey. Issue 2 (February 2023)
- Record Type:
- Journal Article
- Title:
- Emotional reactions to infertility diagnosis: thematic and natural language processing analyses of the 1000 Dreams survey. Issue 2 (February 2023)
- Main Title:
- Emotional reactions to infertility diagnosis: thematic and natural language processing analyses of the 1000 Dreams survey
- Authors:
- Boivin, Jacky
Oguz, Mustafa
Duong, Mai
Cooper, Owen
Filipenko, Dina
Markert, Marie
Samuelsen, Carl
Lenderking, William R. - Abstract:
- Highlights: Infertility diagnoses negatively affect patients and partners, men and women. Emotional reactions point to significant cognitive threat appraisals Diagnoses can trigger recollection of prior reproductive events or concerns Natural language processing is an efficient text analysis tool in fertility care Abstract: Research question: What are the emotional effects of infertility on patients, partners, or both, and how can qualitative thematic analyses and natural language processing (NLP) help evaluate textual data? Design: A cross-sectional, multi-country survey conducted between March 2019 and May 2019. A total of 1944 patients, partners, or both, from nine countries responded to the open-ended question asking about their initial feelings related to an infertility diagnosis. A mixed-method approach that integrated NLP topic modelling and thematic analyses was used to analyse responses. Sentiment polarity was quantified for each response. Linear regression evaluated the association between patient characteristics and sentiment negativity. Results: Common emotional reactions to infertility diagnoses were sadness, depression, stress, disappointment, anxiety, frustration, confusion and loss of self-confidence. NLP topic modelling found additional reactions, i.e. shared feelings with partners, recollections about causes of infertility and treatment experience. Responses to the open-ended question were brief (median: three words) with 71.8% conveying negativeHighlights: Infertility diagnoses negatively affect patients and partners, men and women. Emotional reactions point to significant cognitive threat appraisals Diagnoses can trigger recollection of prior reproductive events or concerns Natural language processing is an efficient text analysis tool in fertility care Abstract: Research question: What are the emotional effects of infertility on patients, partners, or both, and how can qualitative thematic analyses and natural language processing (NLP) help evaluate textual data? Design: A cross-sectional, multi-country survey conducted between March 2019 and May 2019. A total of 1944 patients, partners, or both, from nine countries responded to the open-ended question asking about their initial feelings related to an infertility diagnosis. A mixed-method approach that integrated NLP topic modelling and thematic analyses was used to analyse responses. Sentiment polarity was quantified for each response. Linear regression evaluated the association between patient characteristics and sentiment negativity. Results: Common emotional reactions to infertility diagnoses were sadness, depression, stress, disappointment, anxiety, frustration, confusion and loss of self-confidence. NLP topic modelling found additional reactions, i.e. shared feelings with partners, recollections about causes of infertility and treatment experience. Responses to the open-ended question were brief (median: three words) with 71.8% conveying negative sentiments. Some respondent characteristics showed small but significant associations with sentiment negativity, i.e. country (Spain, China and France were more negative than the USA, P < 0.001, P < 0.003 and P < 0.009 respectively), treatment engagement (no treatment was more negative than one or more treatment, P = 0.027) and marital status (missing/other was more negative than divorced, P = 0.003). Conclusion: Infertility diagnoses create an emotional burden for patients and partners. The mixed-method approach provides a compelling synergy in support of the validity of these findings and shows potential for these techniques in future research. … (more)
- Is Part Of:
- Reproductive biomedicine online. Volume 46:Issue 2(2023)
- Journal:
- Reproductive biomedicine online
- Issue:
- Volume 46:Issue 2(2023)
- Issue Display:
- Volume 46, Issue 2 (2023)
- Year:
- 2023
- Volume:
- 46
- Issue:
- 2
- Issue Sort Value:
- 2023-0046-0002-0000
- Page Start:
- 399
- Page End:
- 409
- Publication Date:
- 2023-02
- Subjects:
- Emotional burden -- Infertility -- Multi-country survey -- Natural language processing -- Sentiment -- Thematic analysis
Human reproductive technology -- Periodicals
Human embryo -- Periodicals
Reproduction -- Periodicals
616.692 - Journal URLs:
- http://www.rbmonline.com/ ↗
http://www.sciencedirect.com/science/journal/14726483 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.rbmo.2022.08.107 ↗
- Languages:
- English
- ISSNs:
- 1472-6483
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7713.705600
British Library DSC - BLDSS-3PM
British Library STI - Digital store
British Library STI - ELD Digital store - Ingest File:
- 25682.xml