Addressing the Shortage of Health Professionals in Official Language Minority Communities to Strengthen Retention Strategies for the Benefit of New Brunswick Francophone and Acadian Communities: Protocol for a Mixed Methods Design
Notice bibliographique
Résumé
BACKGROUND: COVID-19 has highlighted already existing human resource gaps in health care systems. New Brunswick health care services are significantly weakened by a shortage of nurses and physicians, affecting regions where Official Language Minority Communities (OLMCs) reside. Since 2008, Vitalité Health Network (the "Network"), whose work language is French (with services delivered in both official languages, English and French), has provided health care to OLMCs in New Brunswick. The Network currently needs to fill hundreds of vacant physician and nurse positions. It is imperative to strengthen the network's retention strategies to ensure its viability and maintain adequate health care services for OLMCs. The study is a collaborative effort between the Network (our partner) and the research team to identify and implement organizational and structural strategies to upscale retention. OBJECTIVE: The aim of this study is to support one of New Brunswick health networks in identifying and implementing strategies to promote physician and registered nurse retention. More precisely, it wishes to make 4 important contributions to identify (and enhance our understanding of) the factors related to the retention of physicians and nurses within the Network; determine, based on the "Magnet Hospital" model and the "Making it Work" framework, on which aspects of the Network's environment (internal or external) it should focus for its retention strategy; define clear and actionable practices to help the Network replenish its strength and vitality; and improve the quality of health care services to OLMCs. METHODS: The sequential methodology combines quantitative and qualitative approaches based on a mixed methods design. For the quantitative part, data collected through the years by the Network will be used to take stock of vacant positions and examine turnover rates. These data will also help determine which areas have the most critical challenges and which ones have more successful approaches regarding retention. Recruitment will be made in those areas for the qualitative part of the study to conduct interviews and focus groups with different respondents, either currently employed or who have left it in the last 5 years. RESULTS: This study was funded in February 2022. Active enrollment and data collection started in the spring of 2022. A total of 56 semistructured interviews were conducted with physicians and nurses. As of manuscript submission, qualitative data analysis is in progress and quantitative data collection is intended to end by February 2023. Summer and fall 2023 is the anticipated period to disseminate the results. CONCLUSIONS: Applying the "Magnet Hospital" model and the "Making it Work" framework outside urban settings will offer a novel outlook to the knowledge of professional resource shortages within OLMCs. Furthermore, this study will generate recommendations that could contribute to a more robust retention plan for physicians and registered nurses. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41485.
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 | 0,074 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,004 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,068 | 0,012 |
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 ».