Nurse’s clinical skill utilisation: An opinion from public health institutions
Bibliographic record
Abstract
Abstract Background Nurses are the backbone of health care systems worldwide. In India the assessment of existing knowledge of nursing workforce and the utilization of skills is not evaluated and properly used to ensure good quality in health care. Methods Using the Delphi technique a survey was developed and sent to nurses. Self- rating methods (on a likert scale) were used in order to operationalize the personal skills. Results Almost half (48%) of the participants have a Bachelor degree. Out of this 27.2% qualified for a higher education (e.g. Master in related subject). Most nurses (56% in sample were females) are permanent employed working as staff nurse or nursing officers in the public sector. Among the participants 20% have sufficient teaching experience between 1 to 3 years. Self-rating of skills was high in almost all topics. Conclusion Having attained higher education most of the participants remain working as staff nurses. The good self-rating of participants underlines their ability to take over much higher positions and responsibilities. Moreover, teaching experience is hardly acknowledged by institutions since teaching staff is usually recruited from outside. The study suggests that a majority of the population has an interest to work in rural area. Better work conditions are needed in order to gain workforce in this areas.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".