Assessment of clinician well-being and the factors that influence it using validated questionnaires: a systematic review
Notice bibliographique
Résumé
Measuring clinician experiences of care and well-being (e.g. job satisfaction, fulfillment) offers insights into the practice environment’s impact, aiding workforce retention, patient safety, and care quality. However, valid measurement instruments are essential. This systematic review identified validated self-reported questionnaires designed to assess clinician well-being and its influencing factors. Psychometric studies in English or French on measurement instruments addressing factors that influence clinician well-being, as proposed by the National Academy of Medicine, were included. Studies published between 2013 and 2023 were retrieved in December 2023 by searching these databases: CINAHL, Embase, HaPI, MEDLINE, PsycINFO, Mental Measurements Yearbook, and APA PsycTests. Study selection was completed by two independent reviewers. Results were summarized narratively, in tables, and figures. Quality of psychometric studies was assessed by the number of measurement properties addressed. The review protocol was registered with INPLASY® (202410047). Out of 10,441 records identified, 136 studies are included. The majority come from the USA (27.2%), Spain (11.0%), Canada (5.9%), or Australia (5.9%). Most focus on instruments for clinicians, regardless of their specialty (55.9%). Among profession-specific instruments (44.1%), nurses and physicians are mainly targeted. The most common domains are: (1) ‘Learning/practice environment’ (38.2%), (2) ‘Healthcare responsibilities’ (21.3%), and (3) ‘Organizational factors’ (19.1%). The most frequently addressed measurement properties are: (1) Internal consistency (88.2%), (2) Structural validity (75.7%), and (3) Content validity (68.4%). Many tools for measuring clinician well-being exist, but few are fully validated. The results of this review provide a foundation to support ongoing psychometric evaluation and cross-cultural adaptation. Audet et al. systematically review the literature aimed at identifying validated self-reported questionnaires designed to assess clinician well-being and its influencing factors. While several tools exist, few have undergone comprehensive validation. Society and healthcare services are evolving rapidly, requiring clinicians to constantly adapt and placing them under continuous pressure. It is essential to investigate the factors influencing their well-being at work to maintain safe and high-quality patient care. To achieve this, valid tools are needed to measure clinician well-being. We conducted a literature review to identify tools currently available worldwide. Our results show that most of these tools are in English and originate from the United States. Moreover, a large proportion of tools focus on physicians and nurses. Given that healthcare organization varies between countries, it is important to have valid tools adapted to each country’s cultural context and language. We therefore identify a need for cross-cultural adaptation of these tools into multiple languages and care settings. Additionally, there should be profession-specific tools for various healthcare providers (e.g., pharmacists, dentists, physiotherapists), not only for physicians and nurses. Improvements to these tools will enable better assessment of health worker wellbeing, which will have a positive impact on them and the patients they treat.
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 | 0,023 | 0,101 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,007 |
| Bibliométrie | 0,013 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».