Development of the Creating Comfort in Choice Theory of Decision Making Regarding Antidepressant Use in Pregnancy: “The Biggest Decision I’ve Ever Made”
Bibliographic record
Abstract
Prenatal depression affects approximately 10% to 15% of women. Guidelines recommend supporting women to make informed treatment decisions; however, minimal evidence exists regarding this decision-making process. This study aimed to develop a constructivist grounded theory of prenatal antidepressant treatment decision-making. Semi-structured interviews were conducted with purposively sampled women from the community or specialty clinics (N = 31). Iterative data collection and analysis, theoretical sampling, and member checking supported model sufficiency. In the Creating Comfort in Choice theory that we developed, participants were highly conscious of societal stigma toward mental illness and prenatal medication use, so fear, anxiety, and guilt dominated decision-making. Participants navigated dynamically among three clusters of decision-making activities: seeking information, making sense of information, and self-soothing. “Seeking information” included internal and external processes. In “making sense of information,” participants appraised available evidence. In “self-soothing,” participants engaged in coping strategies to try to alleviate painful emotions. The Creating Comfort in Choice theory can support patient-oriented decision-making regarding prenatal mental healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".