Survey investigating factors affecting recruitment and retention in the UK veterinary nursing profession
Bibliographic record
Abstract
BACKGROUND: Recruitment and retention have been identified as contributing factors to workforce shortages in the veterinary team. METHODS: Results from veterinary nurses to an online questionnaire regarding recruitment and retention were analysed. RESULTS: Veterinary nurses had few job changes (median 2); however, 53.8% (n = 1060) reported they were likely or very likely to leave their employment within 2 years. Respondents who were recently qualified (p < 0.001) and on lower salaries (p < 0.001) were significantly more likely to plan to leave. The most frequently chosen reasons to stay in a position were team, location and working hours, while reasons to leave were salary, management and work-life balance. Respondents most disliked 'dealing with people', remuneration and work-life balance and would like to change the salary, management and team aspects. Employers reported difficulty in employing an experienced veterinary nurse. LIMITATIONS: A questionnaire simplifies the nature of retention. Also, a comparatively low number of responses was received, with overrepresentation of some groups. It was conducted in 2018; however, it still provides a useful comparison for studies regarding recent world events. CONCLUSION: The shortage of veterinary nurses is due in part to the lack of retention within the profession. Adequate recompense for work undertaken and value attributed to the role are suggested as contributing factors.
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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 | 0.019 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".