The effect of preceptorship on nurses' training and preparation with implications for Qatar: A literature review
Bibliographic record
Abstract
Background and objective: The Primary Health Care Corporation in Qatar was established in 2012 and is comprised of 23 Health Centers. One of its goals is to create excellence in its workforce. A preceptorship program needs to be initiated at the Primary Health Care Corporation to ensure a high level of training for its nurses. The purpose of these preceptorship programs is to ensure nurses are equipped to carry out Qatar’s National Health Strategy and in doing so the Primary Health Care Corporation has this as its goal. This study amis to assess the effectiveness of preceptorship program models that can eventually be used for adoption as training programs for nurses in Health Centers in Qatar.Methods: A literature review of twenty articles published between 2006 and 2017 that focused on different models of preceptorship programs was conducted. The Mixed Methods Appraisal Tool was used to assess the quality of these studies. The data was analyzed by categorizing the included articles in a matrix sheet based on study design.Results and conclusions: Preceptorship programs are effective in four key areas: increasing nursing knowledge, supporting effective and safe care delivery by newly graduated nurses, increasing organizational support, and decreasing turnover rate and cost.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".