057 Barriers and facilitators in implementing training in shared decision-making based on reflexivity strategies: a qualitative approach
Bibliographic record
Abstract
Introduction Reflexivity-based training for healthcare professionals on shared decision-making (SDM) fosters critical thinking, encouraging reflection on one’s personal values while supporting patient needs. We aimed to identify barriers and facilitators in implementing SDM training that used reflexivity strategies in the context of trisomies’ screening. Methods We performed a qualitative descriptive study. Based on a non-probabilistic recruitment approach, we aimed to recruit 20 prenatal service managers across the province of Quebec for 40- to 60-minute semi-structured interviews. Interviews were to be transcribed anonymously with NVivo Pro software and classified using themes identified from the Regmi and Jones Framework (2020). Deductive analysis was performed, with new themes added in the analysis as they emerged. We followed the Standards for Reporting Qualitative Research guideline. Results Sixteen managers from 14 prenatal services participated. Among them, 56.3% were nurses, 87.5% were women, and 37.5% had a master’s degree. Participants mentioned that the current organizational context is the main challenge for integrating such training into Quebec’s healthcare system. However, the format of the training, online and asynchronous, coupled with learners’ existing technological competence due to pandemic-related experiences, were cited as facilitators for prenatal services to overcome the organizational barriers. Additionally, the diverse profiles and positive reputation of developers, learners’ motivation, and the relevance of training themes were perceived as significant facilitators. Depending on the type of healthcare professional, some liked the reflexivity exercises while others found them challenging. Discussion Quebec’s current organizational context presents obstacles to integrating reflexivity-based training into prenatal services, but existing resources and strategies could be marshalled to overcome these challenges. Conclusion This study is the first to document facilitators and barriers in implementing an online SDM training incorporating reflexivity strategies. Future research could involve comparative analyses among different professional groups.
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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 | 0.038 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".