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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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 | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».