The longitudinal effects of HIWP on new nurses' retention: The role of professional self-image.
Bibliographic record
Abstract
This paper examines the moderating role of nurses' professional self-image in the relationships between high involvement work practices (HIWP) and organizational and professional retention. Using a sample of 185 new nurses and a longitudinal design in which nurses' expectations and professional self-image were measured before they had been contaminated by the organizational context, the paper tests the dynamic nature of the exit process in the beginning of the nurses’ career. The presence of HIWP within the organization, professional self-image, and intention to leave the organization and the profession were measured in time 2 and allow us to understand the effect of the change. Finally, in Time 3, a third questionnaire was administered in order to record the number of nurses who had left the organization and/or the profession. The results obtained suggest that the change between the nurses’ expectations regarding HIWP and the perception of their presence in the workplace is positively related with a change in professional self-image. This professional self-image would have an impact on departure from the organization and from the profession through intention to leave the organization. In term of practical implications, the HIWP have an important impact on new nurses’ retention but there is a good reason to question the role education plays in creating expectations among new graduate nurses. The reality shock, the difference between the expectation and the reality, is the starting point of the withdrawal process.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".