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Record W2397012744 · doi:10.22605/rrh3126

Impact of the Northern Studies Stream and Rehabilitation Studies programs on recruitment and retention to rural and remote practice: 2002-2010

2015· article· en· W2397012744 on OpenAlexafffundabout
Christopher Winn, Brock Chisholm, Jackie Hummelbrunner, Joyce Tryssenaar, Liane Kändler

Bibliographic record

VenueRural and Remote Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsLakehead UniversityNOSM UniversityMcMaster University
FundersMcMaster University
KeywordsRehabilitationRural areaMedicineGraduation (instrument)DemographicsEconomic shortageWork (physics)WorkforceMandateRetention rateFamily medicineMedical educationNursingPhysical therapyDemographyBusinessPolitical science

Abstract

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INTRODUCTION: A shortage of rehabilitation practitioners in rural and/or remote (rural/remote) practice areas has a negative impact on healthcare delivery. In Northern Ontario, Canada, a shortage of rehabilitation professionals (audiology, occupational therapy, physiotherapy, speech-language pathology) has been well documented. In response to this shortage, the Northern Studies Stream (NSS) and Rehabilitation Studies (RS) programs were developed with the mandate to increase the recruitment and retention of rehabilitation professionals to Northern Ontario. However, the number of NSS or RS program graduates who choose to live and work in Northern Ontario or other rural/remote areas, and the extent to which participation in these programs or other factors contributed to their decision, is largely unknown. METHODS: Between 2002 and 2010, a total of 641 individuals participated in the NSS and RS programs and were therefore eligible to participate in the study. Current contact information was obtained for 536 of these individuals (83.6%) who were eligible to participate in the study. An internet-hosted survey was administered in June of 2011. The survey consisted of 48 questions focusing on personal and professional demographics, postgraduate practice and experience, educational preparation, and factors affecting recruitment and retention decisions. RESULTS: A total of 280 respondents completed the survey (response rate 52%). Of these, 95 (33.9%) reported having chosen rural or remote practice following graduation. Multiple factors predictive of recruitment and retention to rural/remote practice were identified. Of particular note was that individuals raised in a rural or remote community were 3.3 times more likely to work in a rural or remote community after graduation. Recruitment was strongly associated with length of time immersed in rural/remote education settings and to participation in the NSS academic semester. Job satisfaction, professional networking opportunities, and rural lifestyle options were identified as important factors for retention in rural/remote practice areas. CONCLUSIONS: The NSS and RS programs have experienced encouraging recruitment outcomes in the past 10 years. Recruitment and retention of rehabilitation therapists to rural/remote locations appears to be positively and significantly affected by the origins of the health professional. The completion of both academic and clinical education in a rural/remote setting and longer duration of rural/remote education were positively associated with an increased likelihood of choosing to practice in a rural/remote area following entry to practice. These findings have potential implications for admission criteria to rehabilitation education programs with a rural curriculum focus as well as implications for postgraduate mentorship programs and employers in rural/remote areas.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.912

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.159
GPT teacher head0.505
Teacher spread0.346 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2015
Admission routes3
Has abstractyes

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