Quality of work life of rural emergency department nurses and physicians: a pilot study
Bibliographic record
Abstract
BACKGROUND: Information about recruitment and retention factors and quality of work life (QWL) in rural emergency departments (EDs) is limited. A pilot study was used to determine the feasibility of a large-scale study of these variables in Quebec EDs. METHODS: Two EDs, approximately 10,000 and 30,000 patients per year respectively, were selected as convenience samples. An online survey containing the Quality of Work Life Systemic Inventory (QWLSI; 34 items) and the Recruitment and Retention Factors Questionnaire (39 items) was sent to ED nurses and physicians of these two EDs. Descriptive statistics of percentage, mean and standard deviation and correlations were used to analyse the data. RESULTS: Forty out of 64 eligible workers (62%) gave their consent to participate, but only 20 had completed both questionnaires. Participants' mean age was 42 years (SD = 11.6). The average participants satisfaction with their access to continuing education was low (Mean = 1.6, SD = 0.8). However, their satisfaction with technical resources (Mean = 2.4, SD = 0.7), pre-hospital and inter-hospital transfer services (Mean = 2.5, SD = 0.6), relationships with colleagues (Mean = 2.7, SD = 0.6) and managers (Mean = 2.2, SD = 0.7), work-life balance (Mean = 2.4, SD = 0.6) and emergency patient access to other departments (Mean = 3.7, SD = 0.6) was in the average. The impact of several aspects of the rural environment (e.g. tranquility) on quality of life was also in the average (Mean = 2.5, SD = 0.7). QWL was in the average, excepted subscale 'support offered to employee' for which the QWL was lower. CONCLUSIONS: Data collection was difficult and the larger study will require strategies to improve recruitment such as a paper alternative. The study showed globally good recruitment and retention factors and QWL for these ED nurses and physicians. These results will help hospital administrations better plan initiatives aimed at improving retention and QWL.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".