Investigating Attraction and Retention of Staff Within Public Mental Health Services in Victoria, Australia: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: A large proportion of Australians are affected by mental illness each year, and treatment gaps are well known. To meet current and future demands and enable access to treatment that is safe, effective, and acceptable, a robust and sustainable mental health workforce is required. Factors reported to attract people to work within the mental health sector include aspiring to help others, having an interest in mental health and human behavior, the desire to make a difference and do something worthwhile, personal lived experience, recognition, and value of discipline-specific roles. However, despite the various reasons people enter the public mental health workforce, recruitment and retention continue to be ongoing challenges. To date, there has been limited investigation into understanding which factors are most relevant to the current Victorian workforce. Furthermore, a comparison to health care workers outside of mental health is also needed to better understand the specific needs of staff within the mental health sector. OBJECTIVE: This study aims to explore factors related to attraction, recruitment, and retention of the public mental health workforce in Victoria, Australia. METHODS: The study is a multisite, mixed methods cross-sectional study to be conducted at 4 public hospital services within Victoria, Australia: 2 in metropolitan and 2 in regional or rural locations. Current, previous, and nonmental health workers will be asked to complete a 20-25-minute web-based survey, which is developed based on previous research and offered participation in an optional 30-60-minute semistructured interview to examine personal experiences and perceptions. Both aspects of the project will examine factors related to attraction, recruitment, and retention in the public mental health workforce. Differences between groups (ie, current, past, and nonmental health workers), as well as location, discipline, and health setting will be examined. Regression analyses will be performed to determine the factors most strongly associated with retention (ie, job satisfaction) and turnover intention. Qualitative data will be transcribed verbatim and thematically analyzed to identify common themes. RESULTS: As of May 2023, we enrolled 539 participants in the web-based survey and 27 participants in the qualitative interview. CONCLUSIONS: This project seeks to build on current knowledge from within Australia and internationally to understand role and service/system-related issues of attraction, recruitment, and retention specifically within Victoria, Australia. Seeking up-to-date information from across the health workforce may provide factors specific to mental health by illuminating any differences between mental health workers and health care workers outside of mental health. Furthermore, exploring motivators across health care disciplines and locations to enter, stay in, or leave a role in public mental health settings will provide valuable information to support how the sector plans and develops strategies that are fit for purpose. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48855.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.085 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".