Nurses’ Perceptions on the Overuse of Health Services: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: To examine whether nurses in Israel think there is overuse of health services, the reasons behind the issue, and ways to reduce the overuse. DESIGN: This was a qualitative study using semistructured interviews. A convenience sample of community care nurses from health clinics across Israel was interviewed. Interviews focused on common areas of overuse, outcomes of overuse, causes of overuse, and potential ways to address the issue. Interviews were recorded, transcribed, and analyzed thematically. FINDINGS: Overuse of antibiotics, imaging, blood tests, and prenatal surveillance were cited as main areas of health service overuse. Participants stated that negative outcomes of overuse could be seen at patient, health system, and population levels. Factors influencing overuse included patient satisfaction, physician fears, and insecurities. Potential interventions included improving physicians' diagnostic confidence, increasing appointment times, providing patients with more treatment information, and implementing a unified computerized system across medical institutions. CONCLUSIONS: Nurses mentioned physicians and patients as main actors in influencing overuse; hence, those populations should be researched further. The health system was identified as the responsible party to address the issue. Health system leaders must consider potential barriers, and investigate interventions that match current culture and context within the health system. CLINICAL RELEVANCE: Nurses can play an essential role in limiting overuse and mitigating subsequent harms to patients.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".