What factors influence health professionals to use decision aids for Down syndrome prenatal screening?
Bibliographic record
Abstract
BACKGROUND: Health professionals are expected to engage pregnant women in shared decision making to help them make informed values-based decisions about prenatal screening. Patient decision aids (PtDAs) foster shared decision-making, but are rarely used in this context. Our objective was to identify factors that could influence health professionals to use a PtDA for decisions about prenatal screening for Down syndrome during a clinical pregnancy follow-up. METHODS: We planned to recruit a purposive sample of 45 health professionals (obstetrician-gynecologists, family physicians and midwives) involved in the care of pregnant women in three clinical sites (15 per site). Participating health professionals first watched a video showing two simulated consecutive prenatal follow-up consultations during which a pregnant woman, her partner and a health professional used a PtDA about Down syndrome prenatal screening. Participants were then interviewed about factors that would influence their use of the PtDA. Questions were based on the Theoretical Domains Framework. We performed content analyses of transcribed verbatim interviews. RESULTS: Out of 42 eligible health professionals approached, 36 agreed to be interviewed (86 % response rate). Of these, 27 were female (75 %), nine were obstetrician-gynecologists (25 %), 15 were family physicians (42 %), and 12 were midwives (33 %), with a mean age of 42.1 ± 11.6 years old. We identified 35 distinct factors reported by 20 % or more participants that were mapped onto 10 of the 12 of the Theoretical Domains Framework domains. The six most frequently mentioned factors influencing use of the PtDA were: 1) a positive appraisal (n = 29, 81 %, beliefs about consequences domain); 2) its availability in the office (n = 27, 75 %, environmental context and resources domain); 3) colleagues' approval (n = 27, 75 %, social influences domain); 4) time constraints (n = 26, 72 %, environmental context and resources domain); 5) finding it a relevant source of information (n = 24, 67 %, motivation and goals domain); and 6) not knowing any PtDAs (n = 23, 64 %, knowledge domain). CONCLUSIONS: Appraisal, PtDA availability, peer approval, time concerns, evidence and PtDA awareness all affect whether health professionals are likely to use a PtDA to help pregnant women make informed decision about Down syndrome screening. Implementation strategies will need to address these factors.
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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 | 0.008 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".