Negotiating concepts of evidence‐based practice in the provision of good service for nursing and allied health professionals
Bibliographic record
Abstract
OBJECTIVE: The principles of evidence-based medicine have been critiqued by the 'caring' professions, such as nursing and social work, and evidence-informed medicine has been proposed as a more client-centred, integrative approach to practice. The purpose of this study was to explore how Canadian health science librarians who serve nurses and allied health professionals define good service and how they negotiate evidence-based principles in their searching strategies. METHOD: Twenty-two librarians completed a 30 minute, semi-structured phone interview about strategies for providing good service and supporting evidence-based services. Participants were also asked to respond to three challenging search scenarios. Analysis of results used grounded theory methods. RESULTS: Participants' definitions of good service and strategies for supporting evidence-based practice involved discussions about types of services provided, aspects of the librarian providing the service and aspects of the information provided during the service. Analysis of search scenarios revealed four justifications librarians rely upon when providing evidence that is in opposition to what their patron hopes to receive (evidentiary, ethical, practice-based and boundaries of the profession). CONCLUSION: The findings of this study suggest that health science librarians are both constrained and enabled by the principles of evidence-based medicine and especially by understandings of 'best evidence'.
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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 | 0.225 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.029 | 0.106 |
| Scholarly communication | 0.037 | 0.029 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".