Pilot Study on the Impact of Evidence-Based Data on Oncology Pharmacists’ Perceptions
Bibliographic record
Abstract
OBJECTIVES: The main objective was to evaluate whether the level of agreement of oncology hospital pharmacists with statements on their impact is influenced by the presence or absence of evidence-based data. The secondary objective was to evaluate the relative importance of evidence-based data among factors that may have contributed to oncology pharmacy practice evolution. METHODS: Oncology pharmacists' answered a Web questionnaire to measure their level of agreement with statements regarding their impact. Respondents answered the questionnaire before (pre) and after (post) being informed whether supporting evidence was available for each statement. Respondents were also asked to rank all of the factors in order of their perceived contribution to oncology pharmacy practice evolution. RESULTS: A total of 64 questionnaires were obtained. Respondents reported a high level of agreement with statements regarding their impact on oncology pharmacy practice (mean agreement of 95.9% pre vs 93.8% post). A statistically significant diminution in the level of agreement was observed for 3 statements after respondents were informed that no supporting evidence was available for these statements. Respondents assigned a high importance to factors related to the perception of positive outcomes of pharmaceutical activities on patient safety, health care costs, and clinical results but a low importance to the use of evidence-based data.
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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 | 0.048 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".