Identifying indicators sensitive to primary healthcare nurse practitioner practice: A review of systematic reviews
Bibliographic record
Abstract
AIM: To identify indicators sensitive to the practice of primary healthcare nurse practitioners (PHCNPs). MATERIALS AND METHODS: A review of systematic reviews was undertaken to identify indicators sensitive to PHCNP practice. Published and grey literature was searched from January 1, 2010 to December 2, 2022. Titles/abstracts (n = 4251) and full texts (n = 365) were screened independently by two reviewers, with a third acting as a tie-breaker. Reference lists of relevant publications were reviewed. Risk of bias was examined independently by two reviewers using AMSTAR-2. Data were extracted by one reviewer and verified by a second reviewer to describe study characteristics, indicators, and results. Indicators were recoded into categories. Findings were summarized using narrative synthesis. RESULTS: Forty-four systematic reviews were retained including 271 indicators that were recoded into 26 indicator categories at the patient, provider and health system levels. Nineteen reviews were assessed to be at low risk of bias. Patient indicator categories included activities of daily living, adaptation to health conditions, clinical conditions, diagnosis, education-patient, mortality, patient adherence, quality of life, satisfaction, and signs and symptoms. Provider indicator categories included adherence to best practice-providers, education-providers, illness prevention, interprofessional team functioning, and prescribing. Health system indicator categories included access to care, consultations, costs, emergency room visits, healthcare service delivery, hospitalizations, length of stay, patient safety, quality of care, scope of practice, and wait times. DISCUSSION: Equal to improved care for almost all indicators was found consistently for the PHCNP group. Very few indicators favoured the control group. No indicator was identified for high/low fidelity simulation, cultural safety and cultural sensitivity with people in vulnerable situations or Indigenous Peoples. CONCLUSION: This review of systematic reviews identified patient, provider and health system indicators sensitive to PHCNP practice. The findings help clarify how PHCNPs contribute to care outcomes. PROSPERO REGISTRATION NUMBER: CRD42020198182.
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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 | 0.086 | 0.326 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".