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Record W2898264472 · doi:10.1186/s13104-018-3869-5

A simulated patient evaluation of pharmacist’s performance in a men’s mental health program

2018· article· en· W2898264472 on OpenAlexaff
Andrea Murphy, David M. Gardner

Bibliographic record

VenueBMC Research Notes · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsDalhousie University
FundersMovember Foundation
KeywordsPharmacistMedicinePharmacyMental healthFamily medicinePromotion (chess)NursingPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The Headstrong program, a pharmacy based men's mental health promotion program, was designed to enhance pharmacists' care of men with mental illness and addictions and was focused on six conditions. A simulated patient (SP) encounter on insomnia was used to evaluate pharmacist's performance as a part of the Headstrong program. RESULTS: Six Headstrong pharmacists consented to participate in the SP encounter as part of the evaluation of the Headstrong program. Pharmacists' mean scores in most categories that were evaluated (e.g., pre-supply/assessment score, sleep score) were lower than expected. In assessing the SP during the encounter, pharmacists' mean score was 5.7 (SD 2.0) of a possible 13 points. No pharmacists asked about the SP's age, availability of other supports, allergies, and whether they had an existing relationship with a pharmacist. One pharmacist inquired about medical conditions, and two asked about pre-existing mental health conditions. Three pharmacists inquired about concurrent medications. The Headstrong program was discussed by half of the pharmacists and a resource recommended by the Headstrong program was suggested by one pharmacist. Several pharmacists used self-disclosure as a mechanism to support rapport building. Overall, the SP felt cared for and respected by the pharmacists and had confidence in their knowledge.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.572
GPT teacher head0.621
Teacher spread0.049 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations13
Published2018
Admission routes1
Has abstractyes

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