CHLA 2024 Conference Contributed Papers / ABSC Congrès 2024 Communications Libres
Bibliographic record
Abstract
Introduction: This study delves into the challenges faced by early career researchers (ECRs) and librarians in health professions conducting systematic and scoping reviews.Given the pivotal role of these methodologies in evidence synthesis, understanding the unique experiences of this demographic is crucial.Methods: A mixed methods approach combines quantitative surveys and semi-structured interviews.The structured survey, administered to a diverse sample, examined methodological expertise, resource access, and time constraints.Simultaneously, semi-structured interviews with a subset of participants provided qualitative depth, exploring personal experiences and uncovering facilitators such as mentorship programs, collaborative networks, and specialized training.Results: Preliminary survey findings revealed common challenges, including limited methodological proficiency and resource constraints.Qualitative interviews contextualized these challenges, offering insights into coping strategies and nuanced facilitators that contribute to successful reviews.Conclusion: This research provides actionable recommendations for academic institutions, mentors, and organizations to support ECRs and librarians.By addressing identified barriers and leveraging facilitators, stakeholders can cultivate an environment conducive to high-quality evidence synthesis, advancing research and evidence-based practice in health professions.The integrated findings from both quantitative and qualitative methods offer a comprehensive understanding of the multi-faceted landscape surrounding systematic and scoping reviews in this context-based practice in health professions.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.685 | 0.511 |
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".