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Record W4226020499 · doi:10.1177/26334941211068010

Evaluation of predictor factors of psychological distress in women with unexplained infertility

2022· article· en· W4226020499 on OpenAlexafffundabout
Ingrid Noël, Sylvie Dodin, Stéphanie Dufour, Marie-Ève Bergeron, J Lefèbvre, Sarah Maheux‐Lacroix

Bibliographic record

VenueTherapeutic Advances in Reproductive Health · 2022
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsWilfrid Laurier UniversityUniversité Laval
FundersFonds de Recherche du Québec - Santé
KeywordsAnxietyInfertilityMedicineHospital Anxiety and Depression ScaleOdds ratioUnexplained infertilityDepression (economics)PopulationFertilityLogistic regressionConfidence intervalPsychiatryDistressCross-sectional studyClinical psychologyObstetricsPregnancyInternal medicine

Abstract

fetched live from OpenAlex

Objective: The objective of this study was to establish the frequency of anxiety and depressive symptoms among women diagnosed with unexplained infertility and to identify risk factors. Methods: We conducted a descriptive cross-sectional study. Forty-two women from the CHU de Quebec fertility clinic were recruited. Women completed the ‘Hospital Anxiety and Depression Scale’ (HADS) self-administered questionnaire, used to estimate prevalence of anxiety and depressive symptoms (score ≥ 8). Results: Overall, 55% ( n = 23) of participants were identified with anxiety or depressive symptoms according to the HADS questionnaire. Anxiety symptoms were more frequent (55%) compared with depressive symptoms (10%). According to a logistic regression model, being under 35 years old [odds ratio (OR) = 16.6, confidence interval (CI): 1.9–25.0], never had a previous spontaneous abortion (OR = 5.6, CI: 1.1–43.5) and never sought fertility treatment (OR = 5.5, CI: 1.1–45.4) were associated with a higher risk of anxiety and depressive symptoms. Conclusion: Anxiety and depressive symptoms are common among women with unexplained infertility, and strategies should be developed to better support and treat this high-risk population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.080
GPT teacher head0.424
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes3
Has abstractyes

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