Understanding the Experiences of Clinicians Accessing Electronic Databases to Search for Evidence on Pain Management Using a Mixed Methods Approach
Bibliographic record
Abstract
The act of searching and retrieving evidence falls under the second step of the EBP process-tracking down the best evidence. The purpose of this study is to understand the competencies of clinicians accessing electronic databases to search for evidence on pain management using a mixed methods approach. Thirty-seven healthcare professionals (14 occupational therapists, 13 physical therapists, 8 nurses, and 2 psychologists) who are actively involved in pain management were included. This study involved two parts (a qualitative and a quantitative part) that ran in parallel. Participants were interviewed using a semi-structured interview guide (qualitative data); data were transcribed verbatim. During the interview, participants were evaluated in comparison to a set of pre-determined practice competencies using a chart-stimulated recall (CSR) technique (quantitative data). CSR was scored on a 7-point Likert scale. Coding was completed by two raters; themes across each of the competencies were integrated by three raters. Seven themes evolved out of the qualitative responses to these competencies: formulating a research question, sources of evidence accessed, search strategy, refining the yield, barriers and facilitators, clinical decision making, and knowledge and awareness about appraising the quality of evidence. The qualitative results informed an understanding of the strengths and weaknesses in the competencies evaluated. In conclusion, using a mixed methods approach, we found that clinicians were performing well with their basic literature review skills, but when it came to advanced skills like using Boolean operators, critical appraisal and finding levels of evidence they seem to require more training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".