MétaCan
Menu
Back to cohort
Record W1964531455 · doi:10.2182/cjot.2010.77.3.3

Supporting (re) Entry to Professional Practice: The SEPP Project

2010· article· en· W1964531455 on OpenAlexaffvenueabout
Susan Baptiste, P. Clifford Blais, Christine L. Brenchley, Dorianne Sauvé, Patricia McMahon, Usha Rangachari

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedical educationProfessional developmentOccupational therapyHealth professionalsEntry LevelAllied health professionsMedicinePsychologyNursingHealth carePolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: During the past decade, there has been an increasing awareness of the challenges affecting internationally educated professionals seeking registration in Canada. PURPOSE: To describe a project designed to support entry or re-entry to active practice for occupational therapists and physiotherapists who were internationally educated or seeking a return to practice after a prolonged absence. METHODS: The major objectives of the project were to develop and evaluate a mentoring network model to support therapists entering or re-entering professional practice in Ontario. Online and other resources were used to enhance professional knowledge and build mentored networks. Supervised placement opportunities were also sought for many participants to meet their learning and integration needs. RESULTS: The project achieved its major objectives and highlighted the challenges faced by individuals seeking to (re)enter professional practice in Ontario. IMPLICATIONS: Project outcomes have wide applications across many health professions.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.760

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0020.001
Open science0.0020.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.151
GPT teacher head0.560
Teacher spread0.409 · 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

Citations7
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Occupational TherapySame topicGlobal Health Workforce IssuesFrench-language works237,207