Fostering Integration through a Healthy, Safe Workforce: Eliciting Global Data to Drive Improvement
Notice bibliographique
Résumé
Worldwide, at least a quarter of health care workers report anxiety, depression and burnout symptoms.1 The ability to effectively address human factors including fatigue, stress, and poor communication can foster heightened patient safety – reducing the risk of error and/or adverse events across organizations, regions, and systems. An integrated health care system relies on healthy health care workers who can develop deep relationships with patients as well as functional relationships with colleagues and service providers – resulting in integrated knowledge of systems and social determinants of care. 2 In 2022, Health Standards Organization (HSO) collaborated with Canadian organizations working across the care continuum to pilot a comprehensive, integrated workforce assessment instrument. The HSO Workforce Survey™ captures data on patient/resident/client safety, care quality, work environments and well-being. Informed by research, analysis of pre-existing survey instrument data and client consultations, the survey tool enables health care workers to provide meaningful data on key themes including job characteristics, demographics, leadership, work team, well-being and engagement, patient centred care, and patient safety. In particular, survey results enable organizations to identify sources of risk to patients and their workforce, and equally important, learn about the factors that contribute to outstanding performance. Taking approximately 15 minutes to complete, the instrument facilitates data acquisition on performance and psychological health and safety across all health care sectors. Specifically, the survey maps to the National Standard of Canada for Psychological Health and Safety in the Workplace and provides reports at organizational, regional, and system-levels. This collation of data thus facilitates ongoing learning and continuous quality improvement – providing insights to heighten health care worker performance and drive health care integration. In this presentation, HSO will outline the preliminary results from the 17 health care organizations that participated as early adopters of the survey tool from September to December 2022; this includes hospitals, long-term care, home care, mental health facilities and emergency medical services. Collated findings from 10,064 health care workers, representing a 31% response rate, will be shared along with data on demographics, evidence-based decision making, safety incident reporting as well as health and well-being. The presentation will highlight how data-driven insights can measurably improve the wellbeing of health care workers. Linkages between workforce perceptions and quality outcomes will also be discussed. Furthermore, the relevance of compiling and benchmarking global workforce data to foster better, integrated care will be shared.
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 | 0,140 | 0,167 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,010 | 0,014 |
| Science ouverte | 0,003 | 0,019 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».