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Record 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 on OpenAlexafffundabout
Mary Eleanor Hill, Denise Raftis, Pamela Wakewich

Bibliographic record

VenueRural and Remote Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsNOSM UniversityLakehead University
FundersOntario Ministry of Health and Long-Term Care
KeywordsInternshipWorkforceGraduation (instrument)MedicineRural areaFamily medicineNursingPublic healthMedical educationHealth careFocus groupSociologyPolitical science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.153
GPT teacher head0.411
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2017
Admission routes3
Has abstractyes

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