Theory-based approach to developing an implementation plan to support the adoption of a patient decision aid for Down syndrome prenatal screening
Bibliographic record
Abstract
Abstract Background Our team has developed a decision aid to help pregnant women and their partners make informed decisions about Down syndrome prenatal screening. However, the decision aid is not yet widely available in Quebec’s prenatal care pathways. Objective We sought to identify knowledge translation strategies and develop an implementation plan to promote the use of the decision aid in prenatal care services in Quebec, Canada. Methods Guided by the Knowledge-to-Action Framework and the Theoretical Domains Framework, we performed a synthesis of our research (11 publications) on prenatal screening in Quebec and on the decision aid. Two authors independently reviewed the 11 articles, extracted information, and mapped it onto the Knowledge-to-Action framework. Using participatory action research methods, we then recruited pregnant women, health professionals, managers of three prenatal care services, and researchers to (a) identify the different clinical pathways followed by pregnant women and (b) select knowledge translation strategies for a clinical implementation plan. Then, based on all the information gathered, the authors established a consensus on strategies to include in the plan. Results Our knowledge synthesis showed that pregnant women and their partners are not sufficiently involved in the decision-making process about prenatal screening and that there are numerous barriers and facilitators of the use of the decision aid in clinical practice (e.g., low intention to use it among health providers). Using a participatory action approach, we met with five pregnant women, three managers, and six health professionals. They informed us about three of Quebec’s prenatal care pathways and helped us identify 20 knowledge translation strategies (e.g., nurse discusses decision aid with women before they meet the doctor) to include in a clinical implementation plan. The research team reached a consensus about the clinical plan and also about broader organizational strategies, such as training healthcare providers in the use of the decision aid, monitoring its impact (e.g., measure decisional conflict) and sustaining its use (e.g., engage key stakeholders in the implementation process). Conclusion Next steps are to pilot our implementation plan while further identifying global strategies that target institutional, policy, and systemic supports for implementation.
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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 | 0.139 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".