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Record W3015531300 · doi:10.1080/24740527.2020.1749516

Community pharmacists and chronic pain: A qualitative study of experience, perception, and challenges

2020· article· en· W3015531300 on OpenAlexafffund
Hamed Tabeefar, Feng Chang, Martin Cooke, Tejal Patel

Bibliographic record

VenueCanadian Journal of Pain · 2020
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Health and Long-Term CareGovernment of Ontario
KeywordsThematic analysisGrounded theoryQualitative researchHarmPerceptionChronic painMedicineHealth careFamily medicineNursingPsychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Background: Patients suffering from chronic pain frequently ask pharmacists for advice.Aims: This study was prompted by inadequacies in the available body of literature reporting on pharmacists’ experiences with providing care for patients with chronic pain in the community setting.Methods: A qualitative investigation of Ontario community pharmacists’ experiences was carried out. Participants were interviewed using a semistructured guide. Interviews were analyzed using thematic analysis, influenced by grounded theory.Results: This study revealed that pharmacists were knowledgeable and empathetic toward patient concerns. Challenges in their role included financial factors, patient access to multimodal treatment options, potential for harm associated with opioid use, inadequate monitoring, and gaps in training.Conclusions: This study reports community and Family Health Team pharmacists’ experiences caring for patients with chronic pain and perceptions of their professional role, including strengths and limitations, and identifies perceived challenges in the health care system.

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)0.006
metaresearch head score (Gemma)0.018
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.003
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 head0.350
GPT teacher head0.450
Teacher spread0.101 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

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