How much do family physicians involve pregnant women in decisions about prenatal screening for Down syndrome?
Bibliographic record
Abstract
OBJECTIVE: To assess the extent to which family physicians (FPs) involve women in decisions about prenatal screening for Down syndrome. METHODS: Based on transcripts of consultations between 41 FPs and 128 women, two raters independently assessed clinician's efforts to involve women in decisions about prenatal screening for Down syndrome using the French-language version of OPTION. Descriptive statistics of OPTION scores were calculated. Construct validity was assessed by performing a principal factor analysis and by measuring association with consultation duration and FPs sociodemograhics. Internal consistency was assessed with Cronbach's alpha and inter-rater reliability with the intraclass correlation coefficient. RESULTS: The overall mean OPTION score was low: 19 +/- 7 (range = 0 [no involvement] to 100 [high involvement]). One factor accounted for 80% of the variance. Both internal consistency and inter-rater reliability were very good (Cronbach's alpha = 0.73; ICC = 0.76). OPTION scores were lower for residents than for licensed FPs (17 +/- 5 vs 21 +/- 4; p = 0.02) and were positively associated with duration of consultation (r = 0.56; p < 0.001). CONCLUSION: Based on the French-language version of OPTION, which showed satisfactory psychometric properties, FPs studied put minimal efforts to involve women in decisions about prenatal screening for Down syndrome.
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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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".