Assessing healthcare organisations’ readiness to implement a learning health system: protocol for questionnaire validation using a Delphi method
Bibliographic record
Abstract
INTRODUCTION: In the health sciences, it can take up to 17 years for 14% of research findings to be adopted in clinical practice. Adopting a learning health system (LHS) approach may help accelerate the transition of medicoadministrative and clinical data to knowledge, knowledge to performance and performance to data. However, little is currently known about whether healthcare organisations are both willing and able to adopt such an innovation. Therefore, the aim of this study is to generate validity evidence in support of a measure assessing healthcare organisations' readiness to implement an LHS approach. METHODS AND ANALYSIS: A three-round Delphi method will be used to establish consensus on the relevance, clarity and comprehensiveness of the LHS readiness questionnaire's domains, subdomains and items. The questionnaire has been developed based on a review of the literature. Participants with expertise in LHS across Canada and internationally will be purposively recruited using a modified Dillman approach, with opportunities for additional snowball sampling. Descriptive statistics will be calculated from all closed-ended Delphi survey responses. A conventional content analysis will be conducted on all open-ended responses. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the McGill University Faculty of Medicine and Health Sciences Institutional Review Board (AO3-E23-24B). The findings of this study will be disseminated in peer-reviewed publications, academic conferences, knowledge mobilisation workshops and through policy briefs and position papers.
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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 | 0.173 | 0.132 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".