MétaCan
Menu
Retour à la cohorte
Enregistrement W2584337453 · doi:10.22605/rrh4035

Strengthening the rural dietetics workforce: examining early effects of the Northern Ontario Dietetic Internship Program on recruitment and retention

2017· article· en· W2584337453 sur OpenAlexafffundabout
Mary Eleanor Hill, Denise Raftis, Pamela Wakewich

Notice bibliographique

RevueRural and Remote Health · 2017
Typearticle
Langueen
DomaineHealth Professions
ThématiqueDietetics, Nutrition, and Education
Établissements canadiensNOSM UniversityLakehead University
Organismes subventionnairesOntario Ministry of Health and Long-Term Care
Mots-clésInternshipWorkforceGraduation (instrument)MedicineRural areaFamily medicineNursingPublic healthMedical educationHealth careFocus groupSociologyPolitical science

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: As with other allied health professions, recruitment and retention of dietitians to positions in rural and isolated positions is challenging. The aim of this study was to examine the early effects of the Northern Ontario Dietetic Internship Program (NODIP) on recruitment and retention of dietitians to rural and northern dietetics practice. The program is unique in being the only postgraduate dietetics internship program in Canada that actively selects candidates who have a desire to live and work in northern and rural areas. Objectives of the survey were to track the early career experiences of the first five cohorts (2008-2012) of NODIP graduates, with an emphasis on employment in underserviced rural and northern areas of Ontario. METHODS: NODIP graduates (62) were invited to complete a 27-item, self-administered, mailed questionnaire approximately 22 months after graduation. The survey, reflecting issues identified in the rural allied health and dietetics literature, documented their work history, practice locations, employment settings, roles, future career intentions and rural background. Aggregated data were analyzed descriptively to assess their early work experiences, with a focus on their acceptance of positions in rural and northern communities. Items also assessed professional and personal factors influencing their most recent decisions concerning practice locations. RESULTS: Three-quarters of graduates chose organizations serving rural or northern communities for their first employment positions and two-thirds were practicing in rural and underserviced areas when surveyed. Most worked as clinical, community health or public health dietitians, in diverse settings including clinics, hospitals and diabetes care programs. Although most had found permanent positions, working for more than one employer at a time was not uncommon. Factors affecting practice choices included prior awareness of employers, prospects for full-time employment, flexible working conditions, access to interprofessional practice and continuing education, as well as community and family concerns. Intentions to remain in current positions were also shaped by a mixture of professional and personal considerations. Some would relocate in search of opportunities for specialization; a few would leave due to dissatisfaction with employment conditions and disinterest in work; others would move due to personal and family commitments. CONCLUSIONS: This study provides early evidence that the NODIP distributed and community-engaged learning model has been very successful in its goal of augmenting the rural and northern dietetics workforce, with a majority of graduates accepting and remaining in rural positions during their first 2 years of practice. Whether graduates remain in rural practice, however, depends on a number of other factors, including career aspirations, availability of professional supports and personal commitments. This suggests that additional supports, above and beyond the NODIP internship, may be needed to encourage graduate dietitians to stay in rural and northern practice locations over the longer term.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,694
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,153
Tête enseignante GPT0,411
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2017
Routes d'admission3
Résumé présentoui

Explorer davantage

Même revueRural and Remote HealthMême sujetDietetics, Nutrition, and EducationTravaux en français237 207