Exploring the Distressing Events and Perceptions of Support Experienced by Rural and Remote Nurses: A Thematic Analysis of National Survey Data
Bibliographic record
Abstract
BACKGROUND: Exposure to traumatic events is an occupational hazard with potential adverse psychological consequences. Previous research has focused mainly on urban practice settings; therefore, this study explored the distressing experiences encountered by rural/remote nurses and their perception of organizational support. METHODS: Thematic analyses were conducted on open-ended data from a pan-Canadian survey of 3,822 regulated nurses, where 1,222 nurses (32%) reported experiencing an extremely distressing health care incident within the past 2 years. Among the respondents, 804 nurses (65%) reported that they did not receive psychological support from the organizations leadership following incidents. FINDINGS: Three main themes regarding distressing events were: (a) involvement in profound events of death/dying, traumatic injury and loss, (b) experiencing or witnessing severe violence and/or aggression, and (c) failure to rescue or protect patients/clients. Three themes were identified regarding perceptions of organizational support including: (a) feeling well supported in the work setting with debriefing and reliance on informal peer support, (b) lack of acknowledgement and support from leaders on the nature and impact of distressing events, and (c) barriers influencing access to adequate mental health services in rural/remote settings. CONCLUSION/APPLICATION TO PRACTICE: Findings suggest that rural/remote nurses rely on informal, peer supports; there is a lack of organizational understanding of the potential risks to their psychosocial health and safety. They require more accessible, structured, appropriate, and timely supports within these settings. Increased understanding of the psychological hazards will assist organizations to establish workplace policies and practices designed to protect and support rural/remote nurses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".