PERFECT MATCH: can you fit the personality to the service? Understanding personality traits of hospital pharmacists in different clinical services—an observational study
Bibliographic record
Abstract
OBJECTIVES: The objective of the study was to evaluate if using a validated psychometric test, The Big Five Personality Test, would identify an association between personality traits among hospital pharmacists and certain clinical services. METHODS: This study used a descriptive design. Pharmacists working in a hospital setting providing care to adult patients at the McGill University Health Centre in predefined clinical services (haematology-oncology/oncology clinic, intensive care unit, emergency medicine, internal medicine, geriatrics, infectious diseases) were included. Participants had 10 days to respond to the questionnaire. KEY FINDINGS: Clinical service had a large effect on 'Extraversion' (η2 = 0.336) and 'Conscientiousness' (η2 = 0.297), and there was a significant difference between the group means (P = .025 and P = .05, respectively). A Tukey's post hoc test showed that the significant difference found for 'Extraversion' was mainly due to differences in means between emergency medicine and geriatrics pharmacists (P = .040). Although there was no significant difference between the group means for 'Openness' and 'Neuroticism', clinical service had a large effect on these traits (η2 = 0.283 and η2 = 0.237, respectively). CONCLUSIONS: These results suggest that there is an association between personality traits and clinical services when using a psychometric test, such as the BFPT, as the observed group differences in personality traits (e.g. extraversion, conscientiousness) were associated with large effect sizes. Further research is needed to better understand the practical implications of these traits in the hiring process or the selection of a clinical service.
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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 | 0.003 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".