“ Healthcare organizations’ administrative policies are just too rigid... “: why nurses leave primary and emergency care
Bibliographic record
Abstract
Context. Nursing shortage is a major issue for healthcare systems. Primary and emergency care are no exception. Large scale nurse turnover is costly, is associated with both reduced productivity and poorer patient outcomes. Understanding of the reasons for nurse turnover has become a pressing issue. Objective. To identify the reasons why nurses leave their primary and emergency care position and compare this with other practice areas. Study Design. A descriptive qualitative design using a critical incident technique wase used. Immediately following their resignation, nurses were interviewed to explore the reasons why they had quit their job. Setting. Nurses were recruited from 4 organizations in urban and rural regions of Quebec (CANADA). Population Studied. N=35 nurses were interviewed; n=6 primary care, n=7 emergency care and n=22 from other areas. Instrument. A semi-structured interview guide based on Daouk-Öyry’s (2014) joint model of nurse absenteeism and turnover was used. Outome Measures. The individual, interpersonal, occupational, organizational and national considerations underpinning their decision to quit were analysed. Analysis. The interviews were subjected to thematic content analysis. Critical incident of primary and emergency care nurses were compared to those of the other nurses. Results. All the primary care nurses had at least 5 years’ experience, as did most emergency care nurses, while nurses from other area were less experienced. Primary care nurses were more likely to work as float nurses compared with emergency nurses. Organizational factors (lack of stability, demanding work schedules, work-life balance imbalance) was commonly cited among primary care nurses as to why they resigned from their jobs. Emergency nurses cited individual, interpersonal, and organizational reasons for leaving their jobs. Conclusions. Nurses leaving the primary and emergency care are experienced. The loss of expertise that results from their departure is a major challenge for access to care and services. It is therefore important to take account of their professional dissatisfactions to reduce turnover and improve nurses’ attraction.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".