Iranian staff nurses' views of their productivity and human resource factors improving and impeding it: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Nurses, as the largest human resource element of health care systems, have a major role in providing ongoing, high-quality care to patients. Productivity is a significant indicator of professional development within any professional group, including nurses. The human resource element has been identified as the most important factor affecting productivity. This research aimed to explore nurses' perceptions and experiences of productivity and human resource factors improving or impeding it. METHOD: A qualitative approach was used to obtain rich data; open, semi-structured interviews were also conducted. The sampling was based on the maximum variant approach; data analysis was carried out by content analysis, with the constant comparative method. RESULTS: Participants indicated that human resources issues are the most important factor in promoting or impeding their productivity. They suggested that the factors influencing effectiveness of human resource elements include: systematic evaluation of staff numbers; a sound selection process based on verifiable criteria; provision of an adequate staffing level throughout the year; full involvement of the ward sister in the process of admitting patients; and sound communication within the care team. Paying attention to these factors creates a suitable background for improved productivity and decreases negative impacts of human resource shortages, whereas ignoring or interfering with them would result in lowering of nurses' productivity. CONCLUSION: Participants maintained that satisfactory human resources can improve nurses' productivity and the quality of care they provide; thereby fulfilling the core objective of the health care system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".