Building Capacity for Health Workforce Planning
Notice bibliographique
Résumé
Background: Health workforce planning holds promise for enhancing access to integrated care, supporting proactive and evidence-based workforce decision-making, promoting workforce sustainability, and ensuring equitable distribution of resources. Approach: Ontario Health Toronto and the Canadian Health Workforce Network co-developed and operationalized an integrated primary care workforce planning process in Toronto. The process has four iterative steps that include horizon scanning, scenario generation, workforce modeling, and policy analysis. Engagement with partners is embedded throughout the process. We aimed to spread and scale leading practices in workforce planning and to build capacity for planning by making the approach and resources available to local health system leaders. Using an action-oriented research approach, we recruited five Toronto-area Ontario Health Teams (OHTs) (93 neighbourhoods, .5 million residents) who were interested in receiving dedicated support for integrated primary care workforce planning. Over the course of 200 hours, we worked with 53 health system leaders (with backgrounds in Medicine, Nursing, Physiotherapy and Management) to build capacity for interprofessional planning through education about planning principles, and examination of local population, workforce and system trends. We integrated developmental evaluation - which monitors developments, progress, or advancements during the design or implementation of an innovation, analyzes developments for key learnings, and guides adaptations to improve the innovation - into the process. We provided OHTs with data about local population characteristics and health system use, and tools to support workforce decision-making, and used their feedback to improve planning resources. Results: Engagement and support for interprofessional planning was warmly received by health system leaders who want to make more evidence-informed decisions. Local partners recognized the value of planning and were eager for information to support decision-making but had limited knowledge and confidence. One health system leader expressed appreciation, saying; There's no way we could do this ourselves. Customized data packages validated some community and workforce trends while raising questions about others, and generated requests for additional data. Engagement activities created new connections and potential collaborations and we witnessed spontaneous sharing of ideas and solutions in real-time. A Network of Planners;, which is intended to be a Community of Practice to provide ongoing support for planning, emerged from the engagement process. Implications: A culture of planning is a necessary foundation for addressing health system challenges. Adopting leading practices in interprofessional workforce planning can help providers and system leaders develop a profile of the patients they are serving, help to estimate the services and resources needed and potential service or capacity gaps, identify future emerging issues, facilitate the use of data to monitor progress of implemented strategies and continuous quality improvement, and equip communities to advocate for the resources needed to integrate and optimize patient care. Building capacity for planning through implementing leading practices, including engagement with local partners, can enhance the impact of planning within communities and across the health system, ultimately improving health and wellbeing for all.
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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 | 0,028 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,014 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,003 | 0,023 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,003 |
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 ».