Exploring spillover, turnover intention, burnout and family configuration dynamics among experienced audiologists.
Notice bibliographique
Résumé
Objective: This study addresses the research gap in work-family dynamics among audiologists by examining family structures' influence. It explores the intersection of spillover, burnout, turnover intentions and family structure, an underexplored area in audiology. The study also focuses on four spillover components (negative work-to-family, negative family-to-work, positive work-to-family, and positive family-to-work), to identify key work and family domain predictors and their professional implications. Design: An online questionnaire was administered, comprising self-made questions for the demographics, work and family domain sections. The MIDUS 2 questionnaire was used to examine all four domains of spillover, Maslach's Burnout Inventory was used to assess burnout and the 5-item Turnover Intention Scale from Rahman (2020), an adaptation of Roodt’s (2004) scale, was also utilized to assess turnover intention. Study Sample: 98 clinicians completed the survey, which included 49 parents and 49 non-parents. These clinicians practiced in Australia, Canada, Denmark, Germany, Ireland, Israel, Japan, New Zealand, Portugal, the U.K., and the U.S.A. Results: The results indicate significant differences in all four forms of spillover between parents and non-parents, with NWFS being the only form of spillover showing significant differences between partnered and single parents. Work predictors (work hours, work flexibility, work support, job position, and parental status) revealed that work flexibility had a significant negative association with NWFS and job position showed a significant positive association with NWFS. Parental status also showed a significant negative association with NWFS, with parents experiencing less NWFS than non-parents. When exploring the same work predictors on PWFS, work flexibility showed a significant positive association, while parental status had a significant negative association, showing parents experiencing less PWFS than non-parents. All other predictors were not significant for NWFS or PWFS. Additionally, relationship status, rather than parental status, was used with the same work predictors. Only work flexibility showed a significant negative association with NWFS, while other predictors were not significant. Family predictors (number of children, age of the youngest/only child, family support, and relationship status) indicated that the number of children had a significant negative association with NFWS, while the other predictors were not significant. Additionally, the overall model examining family predictors for PFWS was not significant. When exploring burnout, NWFS was significantly positively associated with emotional exhaustion, whereas PFWS and parental status were significantly negatively associated with emotional exhaustion, with parents experiencing less emotional exhaustion than non-parents. The model examining NWFS and parental status for depersonalization was not significant. Lastly, emotional exhaustion and parental status were significantly positively associated with higher turnover intentions. Similarly, depersonalization and parental status also showed a significant positive relationship with turnover intentions. Both analyses indicate that parents experience greater turnover intentions than non-parents. Conclusion: This study examined spillover, burnout, turnover intentions, and family structure among audiologists. Parental status significantly predicted spillover, while relationship status affected only NWFS. Workplace flexibility and support were also found to be key in reducing negative spillover and enabling positive spillover, whereas family predictors had less impact, which is possibly unique to audiology. Additionally, negative spillover was strongly correlated with emotional exhaustion, while positive spillover mitigated its effects. Parent audiologists also reported lower emotional exhaustion but higher turnover intentions, highlighting work-family balance challenges. Hence, these findings highlight the need for organizational strategies to improve clinician well-being and retention.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».