Diversifying the health workforce: a mixed methods analysis of an employment integration strategy
Notice bibliographique
Résumé
BACKGROUND: Historically, immigration has been a significant population driver in Canada. In October 2020, immigration targets were raised to an unprecedented level to support economic recovery in response to COVID-19. In addition to the economic impact on Canada, the pandemic has created extraordinary challenges for the health sector and heightened the demand for healthcare professionals. It is therefore imperative to accelerate commensurate employment of internationally educated nurses (IENs) to strengthen and sustain the health workforce and provide care for an increasingly diverse population. This study aimed to determine the effectiveness of a project to help job-ready IENs in Ontario, Canada, overcome the hurdle of employment by matching them with healthcare employers that had available nursing positions. METHODS: A mixed methods design was used. Interviews were held with IENs seeking employment in the health sector. Secondary analysis was conducted of a job bank database between September 1 and November 30, 2019 to identify healthcare employers with the highest number of postings. Data obtained from the 2016 Canadian Census were used to create demographic profiles mapping the number and proportion of immigrants living in the communities served by these employers. The project team met with senior executives responsible for hiring and managing nurses for these employers. The executives were given the appropriate community immigrant demographic profile, a manual of strategic practices for hiring and integrating IENs, and the résumés and bios of IENs whose skills and experience matched the jobs posted. RESULTS: In total, 112 IENs were assessed for eligibility and 95 met the inclusion criteria. Twenty-one healthcare employers were identified, and the project team met with 54 senior executives representing these employers. Ninety-five IENs were subsequently matched with an employer. CONCLUSIONS: The project was successful in matching job-ready IENs with healthcare employers and increasing employer awareness of IENs' abilities and competencies, changing demographics, and the benefits of workforce diversity. The targeted activities implemented to support the project goal are applicable to sectors beyond healthcare. Future research should explore the long-term impact of accelerated employment integration of internationally educated professionals and approaches used by other countries.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».