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Record W2141926386 · doi:10.1176/appi.ps.55.12.1434

Community Pharmacists' Attitudes Toward and Professional Interactions With Users of Psychiatric Medication

2004· article· en· W2141926386 on OpenAlexaffabout
Vinay Phokeo, Beth Sproule, Lalitha Raman‐Wilms

Bibliographic record

VenuePsychiatric Services · 2004
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineMental illnessFeelingMental healthPsychiatryReceiptPharmacyPsychiatric medicationFamily medicinePsychology

Abstract

fetched live from OpenAlex

Consumers of psychiatric medications or services may be stigmatized by health care providers. The authors surveyed community pharmacists (N=283) in the greater Toronto area to determine their attitudes toward and professional interactions with patients who used psychiatric medications and those who used cardiovascular medications. Despite generally positive attitudes, pharmacists reported feeling more uncomfortable discussing symptoms and medications with patients who have mental illness than with patients who have cardiovascular problems. Patients with mental illness appeared to receive fewer pharmacy services than patients with cardiovascular disorders. Barriers to receipt of counseling included a lack of privacy and inadequate training. Adequate training in mental health may be key in improving the professional interactions of community pharmacists toward patients who use psychiatric medication.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.077
GPT teacher head0.421
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations164
Published2004
Admission routes2
Has abstractyes

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