A Qualitative Study to Understand the Cultural Factors That Influence Clinical Data Use for Continuing Professional Development
Bibliographic record
Abstract
INTRODUCTION: The use of data to inform lifelong learning has become increasingly important in continuing professional development (CPD) practice. Despite the potential benefits of data-driven learning, our understanding of how physicians engage in data-informed learning activities, particularly for CPD, remains unclear and warrants further study. The purpose of this study was to explore how physicians perceive cultural factors (individual, organizational, and systemic) that influence the use of clinical data to inform lifelong learning and self-initiated CPD activities. METHODS: This qualitative study is part of an explanatory sequential mixed-methods study examining data-informed learning. Participants were psychiatrists and general surgeons from Canada and the United States. Recruitment occurred between April 2019 and November 2019, and the authors conducted semistructured telephone interviews between May 2019 and November 2019. The authors performed thematic analysis using an iterative, inductive method of constant comparative analysis. RESULTS: The authors interviewed 28 physicians: 17 psychiatrists (61%) and 11 general surgeons (39%). Three major themes emerged from the continuous, iterative analysis of interview transcripts: (1) a strong relationship between data and trust, (2) a team-based approach to data-informed learning for practice improvement, and (3) a need for organizational support and advocacy to put data into practice. CONCLUSION: Building trust, taking a team-based approach, and engaging multiple stakeholders, such as data specialists and organizational leadership, may significantly improve the use of data-informed learning. The results are situated in the existing literature, and opportunities for future research are summarized.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".