Investigating Attraction and Retention of Staff Within Public Mental Health Services in Victoria, Australia: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,085 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».