“Walking on both sides of the fence”: A qualitative exploration of the challenges and opportunities facing emergent clinician‐scientists in child health
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: While paediatric clinician-scientists are ideally positioned to generate clinically relevant research and translate research evidence into practice, they face challenges in this dual role. The authors sought to explore the unique contributions, opportunities, and challenges of paediatric clinician-scientists, including issues related to training and ongoing support needs to ensure their success. METHOD: The authors used a qualitative descriptive approach with thematic analysis to explore the experiences of clinician-scientist stakeholders in child health (n = 39). Semi-structured interviews (60 min) were conducted virtually and recorded. Thematic analysis was conducted according to the phases outlined by Braun and Clarke (2006). RESULTS: The analysis resulted in the creation of three themes: (1) "Walking on both sides of the fence": unique positioning of clinician-scientists for advancing clinical practice and research; (2) the clinician-scientist: a specialized role with significant challenges; and (3) beyond the basics of clinical and research training programmes: essential skill sets and knowledge for future clinician-scientists. CONCLUSIONS: While clinician-scientists can make unique contributions to the advancement of evidence-based practice, they face significant barriers straddling their dual roles including divergent institutional cultures in healthcare and academia and a lack of infrastructure to effectively support clinician-scientist positions. Training programmes can play an important role in mentoring and supporting early-career clinician-scientists.
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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 | 0.052 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".