Professional Identity Work of Nurse Practitioners and Family Physicians in Primary Care in Quebec and Ontario – A Study Protocol
Bibliographic record
Abstract
Abstract Background Solo medical practices in primary healthcare delivery have been abandoned in favor of interdisciplinary teamwork in most Western countries. Dynamics in interdisciplinary teams might however be particularly difficult when two or more autonomous health professionals develop similar roles at the practice level. This is the case of family physicians (FPs) and nurse practitioners (NPs), due to the fact that the latter might accomplish not only the traditional role proper to a nurse, but also several medical activities such as requesting diagnostic exams and prescribing medical treatments. The tensions that this overlap might generate and their implications in regard of the development of professional identities, and consequently of the quality of health care delivered, have been suggested, but rarely examined empirically. The goal of this study is to examine identity work, i.e., the processes of (re)construction of professional identities, of NPs and FPs working together in primary care interdisciplinary teams. Methods A longitudinal, interpretive, and comparative multiple (n = 2) case study is proposed. Identity work theory in organizations is adopted as theoretical perspective. Cases are urban primary care multidisciplinary teams from two different Canadian provinces: Quebec and Ontario. Participants are NPs, FPs, managers, and patients. Data gathering involves audio-diaries, individual semi-structured and focus group interviews, observations, and archival material. Narrative and metaphor techniques are adopted for analyzing data collected. Within- and cross-case analysis will be performed. Discussion For practice, the results of this investigation will: (a) be instrumental for clinicians, primary care managers, and policy decision-makers responsible for the implementation of interdisciplinary teamwork in primary healthcare delivery to improve decision-making processes and primary care team performance over time; (b) inform continuing interdisciplinary professional development educational initiatives that support competency in health professionals’ identity construction in interdisciplinary primary care organizations. For research, the project will contribute to enriching theory about identity construction dynamics in health professions, both in the fields of health services and primary care education research.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".