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Record W4411394818 · doi:10.1177/13591053251344508

Community pharmacists’ perceptions of mental health care: A qualitative study on stigma and barriers

2025· article· en· W4411394818 on OpenAlexaff
Mai Rizik, Mohammed Zawiah, Rana Abu Farha

Bibliographic record

VenueJournal of Health Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThematic analysisStigma (botany)Mental healthQualitative researchPharmacyMedicineNursingPsychologyPsychiatry

Abstract

fetched live from OpenAlex

This study aimed to explore the stigma, barriers, and improvements needed for providing effective mental health care in community pharmacies in Jordan. A qualitative study was conducted through semi-structured interviews with 20 community pharmacists. The interviews were conducted in December 2024. Thematic analysis was applied to identify themes. A total of 20 pharmacists were interviewed. Pharmacists reported significant stigma among patients with mental health disorders, often due to societal pressures. However, within the pharmacy environment, pharmacists showed minimal stigma and demonstrated empathy. Key barriers identified included communication challenges, particularly the lack of privacy. Proposed improvements focused on enhancing education and training through workshops and continuous professional development. Community pharmacists play a critical role in mental health care but face challenges such as stigma, communication issues, and knowledge gaps. Addressing these barriers through better training could significantly enhance the ability of pharmacists to provide effective mental health support.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.104
GPT teacher head0.588
Teacher spread0.483 · 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 designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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