Organizational climate in primary care settings: Implications for nurse practitioner practice
Bibliographic record
Abstract
PURPOSE: The purpose of this review is to investigate literature related to organizational climate, define organizational climate, and identify its domains for nurse practitioner (NP) practice in primary care settings. DATA SOURCES: A search was conducted using MEDLINE, PubMed, HealthSTAR/Ovid, ISI Web of Science, and several other health policy and nursingy databases. CONCLUSIONS: In primary care settings, organizational climate for NPs is a set of organizational attributes, which are perceived by NPs about their practice setting, emerge from the way the organization interacts with NPs, and affect NP behaviors and outcomes. Autonomy, NP-physician relations, and professional visibility were identified as organizational climate domains. IMPLICATIONS FOR PRACTICE: NPs should be encouraged to assess organizational climate in their workplace and choose organizations that promote autonomy, collegiality between NPs and physicians, and encourage professional visibility. Organizational and NP awareness of qualities that foster NP practice will be a first step for developing strategies to creating an optimal organizational climate for NPs to deliver high-quality care. More research is needed to develop a comprehensive conceptual framework for organizational climate and develop new instruments to accurately measure organizational climate and link it to NP and patient outcomes.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".