Effectiveness of Patient Decision Aids in Mitigating Infertility Treatment Decision Conflict: A Policy-aware Meta-analysis
Bibliographic record
Abstract
Objectives: To evaluate how fertility support policies moderate the efficacy of patient decision aids (PDAs) in mitigating decision conflict among individuals with infertility, and to characterize the synergistic mechanisms between policy environments and PDA design. Methods: Using the Ottawa Decision Support Framework (ODSF), we conducted an integrated umbrella review and meta-analysis of 18 studies (n = 4,215). Policies were stratified into three support levels (high/medium/low) based on quantifiable indicators such as the reimbursement rate for assisted reproductive technology (ART) and service accessibility. The standardized mean difference (SMD) was pooled via a random-effect model to test the dose-response relationship between policy intensity and PDA effectiveness. Results: PDA significantly mitigated decision conflict (SMD = -0.61, 95% CI [-0.80, -0.42]), with policy support levels accounting for 34.7% of the between-study heterogeneity (β = -0.12, p = 0.03). The effect size in high policy support areas (SMD = -0.82) was significantly greater than in low policy support areas (SMD = -0.35, p = 0.008). While digital PDAs showed higher acceptability in low- and middle-income countries (OR = 2.3), they were associated with a 2.1-fold increased dropout rate, necessitating localized adaptations (such as offline functionality) to bridge the digital divide. Conclusion: Policy support enhances PDA effectiveness through dual pathways of financial relief and institutional trust. A 'policy tier-tool adaptation' model should be constructed, with high support areas focusing on value clarification and low support areas prioritizing cost simulation and community resource integration.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".