Nursing Practice in Primary Care and Patients’ Experience of Care
Bibliographic record
Abstract
PURPOSE: Nurses are identified as a key provider in the management of patients in primary care. The objective of this study was to evaluate patients' experience of care in primary care as it pertained to the nursing role. The aim was to test the hypothesis that, in primary health care organizations (PHCOs) where patients are systematically followed by a nurse, and where nursing competencies are therefore optimally used, patients' experience of care is better. METHOD: Based on a cross-sectional analysis combining organizational and experience of care surveys, we built 2 groups of PHCOs. The first group of PHCOs reported having a nurse who systematically followed patients. The second group had a nurse who performed a variety of activities but did not systematically follow patients. Five indicators of care were constructed based on patient questionnaires. Bivariate and multivariate linear mixed models with random intercepts and with patients nested within were used to analyze the experience of care indicators in both groups. RESULTS: Bivariate analyses revealed a better patient experience of care in PHCOs where a nurse systematically followed patients than in those where a nurse performed other activities. In multivariate analyses that included adjustment variables related to PHCOs and patients, the accessibility indicator was found to be higher. CONCLUSION: Results indicated that systematic follow-up of patients by nurses improved patients' experience of care in terms of accessibility. Using nurses' scope of practice to its full potential is a promising avenue for enhancing both patients' experience of care and health services efficiency.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".