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Record W2170755686 · doi:10.1211/ijpp.16.3.0003

Assessing quality in community pharmacy

2008· article· en· W2170755686 on OpenAlexaboutno aff
Devina Halsall, Darren M. Ashcroft, Peter Noyce

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2008
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePharmacyQuality assuranceQuality (philosophy)Reliability (semiconductor)MEDLINEConstruct validityQuality managementValidityMedical educationFamily medicineNursingPatient satisfactionPsychometricsExternal quality assessmentPathologyService (business)Marketing

Abstract

fetched live from OpenAlex

Abstract Objective This review aimed to identify English-language instruments used to assess quality in community pharmacy and to evaluate their reported validity, reliability, feasibility and acceptability. Method A systematic review was conducted to identify literature relating to the use of instruments to assess quality in community pharmacy. The electronic databases searched included Embase, International Pharmaceutical Abstracts, Medline, e-PIC and Pharmline, covering the period of time between January 1990 and March 2007. Reference lists of identified studies and websites of pharmacy bodies were also searched. Key findings Ten instruments were identified from Canada, Malta, the UK and the US. These were used for quality-assurance and/or quality-improvement purposes and focused on: clinical governance systems; organisational culture/maturity; safety (climate and systems); effectiveness of pharmacy services; and stakeholders' feedback on services. The assessments were at different stages of development, and the majority had not been tested for construct validity, reliability and feasibility. Conclusions Assessments with high validity and reliability give a good indication of the quality of care provided and can indicate areas for improvement. Further research is needed to establish a composite view of quality in community pharmacy; and many of the instruments identified required validation.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0000.002
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 head0.461
GPT teacher head0.601
Teacher spread0.140 · 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 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

Citations8
Published2008
Admission routes1
Has abstractyes

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