A scoping review of weight bias by community pharmacists towards people with obesity and mental illness
Bibliographic record
Abstract
BACKGROUND: Community pharmacists are accessible health care professionals who are increasingly offering weight management programs. People living with serious mental illness have markedly higher rates of obesity and associated illness outcomes than the general population, providing pharmacists who are interested in offering weight management services with an identifiable patient subgroup with increased health needs. Issues with stigma within obesity and mental illness care are prevalent and can lead to inequities and reduced quality of care. METHODS: We conducted a scoping review to map and characterize the available information from published and grey literature sources regarding community pharmacists and weight bias towards obese people with lived experience of mental illness. A staged approach to the scoping review was used. RESULTS: Six articles and 6 websites were abstracted after we removed duplicates and applied our inclusion and exclusion criteria. The published studies that we found indicated that pharmacists and pharmacy students do demonstrate implicit and explicit weight bias. CONCLUSIONS: Very limited research is available regarding weight bias in pharmacists and stigma towards people with obesity, and we found no information on these phenomena relating to people with lived experience of mental illness. Investigations are needed to characterize the extent and nature of anti-fat bias and attitudes by pharmacists and the consequences of these attitudes for patient care.
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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 | 0.028 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".