Psychosocial factors associated with pharmacists’ antidepressant drug treatment monitoring.
Bibliographic record
Abstract
Objective: Patients undergoing antidepressant drug treatment (ADT) may face challenges \nregarding its adverse effects, adherence, and efficacy. Community pharmacists are well \npositioned to manage ADT-related problems. Little is known about the factors influencing \npharmacists’ ADT monitoring. This study aimed to identify the psychosocial factors associated with pharmacists’ intention to perform systematic ADT monitoring and report on \nthis monitoring. \nDesign: Cross-sectional study based on the Theory of Planned Behavior (TPB). \nSetting and participants: Community pharmacists in the province of Quebec, Canada. \nOutcome measures: Pharmacists completed a questionnaire on their performance of ADT \nmonitoring, TPB constructs (intention; attitude; subjective norm; perceived behavioral \ncontrol; and attitudinal, normative, and control beliefs), and professional identity. Systematic ADT monitoring was defined as pharmacists’ reporting 4 or more consultations \nwith each patient during the first year of ADT to address adverse effects, adherence, and \nefficacy. Hierarchical linear regression models were used to identify the factors associated \nwith the intention and reporting of systematic ADT monitoring and Poisson working \nmodels to identify the beliefs associated with intention. \nResults: A total of 1609 pharmacists completed the questionnaire (participation ¼ 29.6%). \nSystematic ADT monitoring was not widely reported (mean score ¼ 2.0 out of 5.0), and \nintention was moderate (mean ¼ 3.2). Pharmacists’ intention was the sole psychosocial factor \nassociated with reporting systematic ADT monitoring (P < 0.0001; R2 ¼ 0.370). All TPB constructs and professional identity were associated with intention (P < 0.0001; R2 ¼ 0.611). \nPerceived behavioral control had the strongest association. \nConclusion: Interventions to promote systematic ADT monitoring should focus on \ndeveloping a strong intention among pharmacists, which could, in turn, influence their \npractice. To influence intention, priority should be given to ensuring that pharmacists \nfeel capable of performing this monitoring. The main barriers to overcome were the \npresence of only 1 pharmacist at work and limited time. Other factors identified offer \ncomplementary intervention targets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".