Research Article Role Clarification Processes for Better Integration of Nurse Practitioners into Primary Healthcare Teams: A Multiple-Case Study
Bibliographic record
Abstract
Copyright © 2014 Isabelle Brault et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Role clarity is a crucial issue for effective interprofessional collaboration. Poorly defined roles can become a source of conflict in clinical teams and reduce the effectiveness of care and services delivered to the population. Our objective in this paper is to outline processes for clarifying professional roles when a new role is introduced into clinical teams, that of the primary healthcare nurse practitioner (PHCNP). To support our empirical analysis we used the Canadian National Interprofessional Competency Framework, which defines the essential components for role clarification among professionals. A qualitative multiple-case study was conducted on six cases in which the PHCNP role was introduced into primary care teams. Data collection included 34 semistructured interviews with key informants involved in the implementation of the PHCNP role. Our results revealed that the best performing primary care teams were those that used a variety of organizational and individual strategies to carry out role clarification processes. From this study, we conclude that role clarification is both an organizational process to be developed and a
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".