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Record W2561947558 · doi:10.1080/13561820.2016.1249281

Expanding pharmacy roles and the interprofessional experience in primary healthcare: A qualitative study

2016· article· en· W2561947558 on OpenAlexaff
Andrea Silvaggi, Shereen Nabhani‐Gebara, Scott Reeves

Bibliographic record

VenueJournal of Interprofessional Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWestern University
Fundersnot available
KeywordsPharmacistThematic analysisInterprofessional educationNursingHealth carePharmacyExploratory researchMedicineQualitative researchWorkloadFocus groupMedical educationPsychology

Abstract

fetched live from OpenAlex

The pharmacist role is undergoing significant changes which are reshaping the way primary healthcare is delivered throughout England. Due to increased physician workload and focus on primary healthcare, the pharmacist role has expanded to provide enhanced patient services, integrating into general practice (GP) settings and working more closely as a member of the healthcare team. However, the experiences of pharmacists and team members are yet to be explored. The proposed study aims to explore the experiences, thoughts, and perceptions of a purposive sample of pharmacists, physicians, and nurses working in 10 GP clinics throughout the southeast of England. Interprofessional relationships, power dynamics, changing professional roles, and barriers and facilitators to the integration of the pharmacist role will be explored. An exploratory multiple case study design will be used to investigate interprofessional experiences within and between clinics. In-depth interviews will be completed with each participant. A thematic analysis will identify themes and patterns from the interview data. Results are expected to produce recommendations to help facilitate the integration of pharmacists in their new role and will have implications for interprofessional collaboration and interprofessional education which are important for delivering safe and effective care.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.159
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.540
Teacher spread0.475 · 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 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

Citations18
Published2016
Admission routes1
Has abstractyes

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