Does digital health technology improve physicians’ job satisfaction and work–life balance? A cross-sectional national survey and regression analysis using an instrumental variable
Notice bibliographique
Résumé
OBJECTIVES: To examine the association between physicians' use of digital health technology and their job satisfaction and work-life balance. DESIGN: A cross-sectional nationally representative survey of physicians and probit regression models were used to examine the association between using digital health technology and the probability of reporting high job satisfaction and a good work-life balance. Models included a rich set of covariates, including physicians' personality traits, and instrumental variable analysis was used to control for bias from unobservable confounders and reverse causality. SETTING: Clinical practice settings in Australia, including physicians working in primary care, hospitals, outpatient settings, and physicians working in the public and private sectors. PARTICIPANTS: Respondents to wave 11 (2018-2019) of the Medicine in Australia: Balancing Employment and Life (MABEL) longitudinal survey of doctors. The analysis sample included a broadly nationally representative sample of 7043 physicians, including general practitioners, specialists and physicians in training. PRIMARY AND SECONDARY OUTCOME MEASURES: The proportion of respondents who used any digital health technology; proportion answered 'moderately satisfied' or 'very satisfied' to the statement on job satisfaction: 'Taking everything into account, how do you feel about your work'; proportion agreeing or strongly agreeing to the statement on work-life balance: 'The balance between my personal and professional commitments is about right.' RESULTS: Physicians with positive beliefs about the effectiveness of using digital health technology were 3.8 percentage points (95% CI 2.7 to 5.0) more likely to use digital health technology compared with those who did not. Physicians with colleagues who already used digital health technology were also 4.1 percentage points (95% CI 2.6 to 5.6) more likely to use digital health technology. The availability of IT support and lack of privacy concerns increased the probability of using digital health technology by 1.6 percentage points (95% CI 1.0 to 2.3) and 0.5 percentage points (95% CI 0.1 to 1.0). Physicians who used digital health technology were 14.2 percentage points (95% CI -1.3 to 29.7) and 20.3 percentage points (95% CI 2.4 to 38.1) more likely to report respectively higher job satisfaction and good work-life balance, compared with the physicians who did not use it. CONCLUSIONS: Findings suggested digital health technology served more as a work resource than work demand for physicians who used it.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».