New Nurses’ Perceptions of Work Environments and Work Outcomes in Mental Health Practice Settings: A Multi-method Study
Bibliographic record
Abstract
Background: New graduate nurses (NGNs) are a vital resource to the nursing workforce given the forecasted staffing trends. NGNs face complex work environments in addition to the challenges inherent in transitioning to practice that shape their initial work experiences. The mental health (MH) practice setting, in particular, has been under-examined with respect to NGNs’ perceptions of the work environment (WE), as have work outcomes. Objectives: This study: 1) identified the perceptions of NGNs working in a MH WE; 2) explored their perceptions of MH nursing culture; 3) determined the extent to which NGNs in MH settings experience job satisfaction (JS), burnout (BO) and turnover intention (TI), and 4) examined whether relationships exist between the MH WE and work outcomes. Methods: A multi-method research design was undertaken with NGNs practicing in MH settings in Ontario, Canada and included the use of a survey of 165 NGNs and interviews with a sample of 15 participants. Nurses working in MH settings with three-years or less of experience were recruited from the College of Nurses of Ontario (CNO) registry. Results: Quantitative and qualitative data were considered independently and also triangulated. Survey data found that NGN working in MH settings perceive the quality of the WE to be favourable, except their participation in the internal governance. JS was insignificant as an explanatory variable relating to TI. Levels of JS, BO and TI were all deemed to be of concern for NGNs. Moreover, nursing WE was found to contribute significantly to said outcomes. Qualitative findings offered further context with themes emerging addressing the rewards/challenges related to caring for patients, such as patient aggression, along with factors in the WE that impact the development of nurse outcomes. Significance: This study resulted in an improved understanding of the WE in MH settings from the lens of NGNs, providing important context regarding what shapes their experience of undesirable work outcomes. These insights and NGN views on MHN facilitates nurse leaders’ understanding of what is needed for NGNs to survive and thrive in a MH practice setting. Strengthening this knowledge will help to support and sustain the MH nursing workforce.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".