Entitativity (‘groupness’): researching the foundation of interprofessional collaboration
Bibliographic record
Abstract
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].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".