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Record W4408140844 · doi:10.1093/ijpp/riaf008

Entitativity (‘groupness’): researching the foundation of interprofessional collaboration

2025· article· en· W4408140844 on OpenAlexaff
Zubin Austin

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineFoundation (evidence)Medical educationNursing

Abstract

fetched live from OpenAlex

Globally, the shift toward more interprofessional and collaborative models of health care delivery is well entrenched [1]. Tools such as expanding scope of practice are used by governments to create more flexible health care delivery and decision-making options and to circumvent strangleholds based on profession-specific activities [2]. In the context of medication therapy management, the proliferation of legislatively enabled ‘prescribing’ roles and responsibilities for professions such as pharmacy, nursing, chiropody, and midwifery illustrates the ways in which health policy makers view decentralizing of authorities traditionally reserved for medical practitioners as a powerful way to enhance efficiency and effectiveness of health services work [2, 3]. Within pharmacy, the move to ‘independent prescribing’ by pharmacists in countries such as the UK and Canada raises important issues and some concerns regarding quality and safety. While there is little doubt that pharmacists have the knowledge and skills to prescribe medications in specific circumstances, real-world limitations (including lack of access to relevant laboratory testing data, or the absence of a central, secure, electronic health record accessible by all health care professionals) can undermine pharmacists’ best efforts in providing best possible patient care [4]. Limitations such as these increase the likelihood that ‘independent’ prescribing may actually increase the risk of creating health care silos in which—literally—the right hand and the left hand are unaware of what each are doing. For example, a patient who wants an antibiotic for a viral infection may be told ‘no’ by a medical practitioner (with access to laboratory testing data) in the morning but be told ‘yes’ by a pharmacist (who does not have access to these data or a shared medical record indicating the medical practitioner’s rationale) in the afternoon. Even when there is an interprofessional shared medical record (as is increasingly common), the risk of profession-specific silos in decision-making can be significant [4]. Such silos are not limited to interprofessional situations: intra-professional collaboration amongst individuals who share the same professional designation can also be similarly isolated (e.g. between hospital-based and community-based pharmacists) [4].

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)0.022
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0070.032
Scholarly communication0.0090.016
Open science0.0030.013
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.057
GPT teacher head0.598
Teacher spread0.541 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractno

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