Contextual Factors Influencing the Evolution of Nurses' Roles in a Primary Health Care Clinic
Bibliographic record
Abstract
OBJECTIVES: The purpose of the research was to explore the everyday experiences and responses of stakeholders of a university-sponsored nurse-managed clinic (CHC) in regard to how nurses' roles in the clinic changed over time and the factors that influenced this change. DESIGN AND SAMPLE: The research used a qualitative interpretive description design to interpret participants' accounts of their experience and perspectives as constructed narratives. The participants (N=23) included clients, community members who were volunteers at the CHC, staff of other community agencies or organizations, and nursing or social work students who had a clinical learning experience at the CHC. MEASURES: Data collection involved two interviews, one semistructured, face-to-face interview at the location selected by the participant, and a group interview held in a boardroom at the CHC. Each interview lasted approximately 60-90 min. RESULTS: The research findings revealed the profound effects of the social, political, and economic context in determining nurses' roles within a nurse-managed primary health care clinic. The evolution of nursing roles occurred in reaction to these effects, causing the nurses within the CHC to juggle their priorities and commitments. CONCLUSIONS: The study provides a contemporary example of the political activism work of nurses that is often invisible and illustrates how the commitment of primary health care nurses to social justice contributes in a significant way to the resolution of health inequities experienced by marginalized populations.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".