Do new roles contribute to job satisfaction and retention of staff in nursing and professions allied to medicine?
Bibliographic record
Abstract
BACKGROUND: Studies have suggested that job dissatisfaction is a major factor influencing nurses' and occupational therapists' intention to leave their profession. It has also been related to turnover of qualified nurses. However, literature relating to these factors among nurses and professions allied to medicine in innovative roles is scarce. AIMS: This paper considers the views of 452 nurses and 162 professionals allied to medicine (PAMs) in innovative roles, on job satisfaction, career development, intention to leave the profession and factors seen as hindering and enhancing effective working. METHODS: A self-completion questionnaire was developed as part of a larger study exploring new roles in practice (The ENRiP Study). FINDINGS: Overall there was a high level of job satisfaction in both groups (nurses and PAMs). Job satisfaction was significantly related to feeling integrated within the post-holder's own professional group and with immediate colleagues, feeling that the role had improved their career prospects, feeling adequately prepared and trained for the role, and working to protocol. Sixty-eight percent (n = 415) of respondents felt the role had enhanced their career prospects but over a quarter of respondents (n = 163; 27%) said they would leave their profession if they could. Low job satisfaction was significantly related to intention to leave the profession. CONCLUSIONS: The vast majority of post-holders in innovative roles felt that the role provided them with a sense of job satisfaction. However, it is essential that the post-holders feel adequately prepared to carry out the role and that the boundaries of their practice are well defined. Career progression and professional integration both being associated with job satisfaction.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".