Hospital pharmacists’ perceived competence in providing care to oncology patients – (HoPP-CoP2)
Bibliographic record
Abstract
BackgroundPrevious research has shown community pharmacists do not have high perceived competence or confidence providing care to patients on oral anti-cancer medications. There is a paucity of evidence when it comes to hospital pharmacists providing care to oncology patients admitted to the hospital for a reason other than cancer.Objective(s)To assess the perceived competence of hospital pharmacists not working in oncology in managing patients taking anticancer drugs. Additionally, identify factors impacting, potential interventions increasing, and any factors that improve perceived competence.MethodsAn anonymous cross-sectional survey distributed to hospital pharmacists throughout Canada.ResultsMean perceived competence results ranged from 2.52 to 3.97 out of 7.00 with respondents reporting most perceived competence managing electrolyte disturbances and least competence managing drug-disease interactions and intervening on hepatic dysfunction. Low confidence in knowledge received from previous oncology training was reported with a mean of 2.82 on the 7-point Likert Scale. Continuing education sessions were perceived as the intervention which would most improve perceived competence with a mean value of 5.92 to 5.94 on a 7-point Likert Scale. The number of CE hours completed in the last five years was the only factor shown to have a statistically significant correlation with perceived competence.ConclusionHospital pharmacists do not perceive themselves as competent in providing care to oncology patients. The implementation of a continuing education program related to oncology may improve perceived and actual competence.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".