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Record W2126474224 · doi:10.1080/13561820220124157

Realizing potential: improving interdisciplinary professional/paraprofessional health care teams in Canada's northern aboriginal communities through education

2002· article· en· W2126474224 on OpenAlexaffabout
Bruce Minore, Margaret Boone

Bibliographic record

VenueJournal of Interprofessional Care · 2002
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsLakehead University
Fundersnot available
KeywordsIndigenousCLARITYHealth careCompetence (human resources)NursingEconomic shortageCultural competenceMedicineMedical educationPublic relationsSociologyPsychologyPolitical sciencePedagogyGovernment (linguistics)

Abstract

fetched live from OpenAlex

To address a shortage of health professional human resources and to overcome cultural barriers, the interdisciplinary health care teams practicing in most northern Canadian aboriginal communities include a number of paraprofessionals recruited locally. This model has great potential to fill service gaps in many rural contexts; there are challenges, however. Drawing from an extensive program of research in indigenous communities in the northwestern part of the Province of Ontario, we identify factors fundamental to effective team functioning: members' clarity about their own and others' roles, appreciation of their respective 'equal but different' knowledge bases, and confidence in one another's competence. We argue for an extension of the information on interdisciplinary practice included in health science education programs to address these issues, thereby enhancing the utility of paraprofessionals within the health human resource mix in rural 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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.411
Teacher spread0.397 · 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 designNot applicable
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

Citations65
Published2002
Admission routes2
Has abstractyes

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