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Record W2170746462 · doi:10.1177/2049463712448174

Interprofessional pain education: definitions, exemplars and future directions

2012· article· en· W2170746462 on OpenAlexaff
Eloise Carr, Judy Watt‐Watson

Bibliographic record

VenueBritish Journal of Pain · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsInterprofessional educationCurriculumHealth professionalsPain managementMedicineHealth careMedical educationProfessional developmentPsychologyPhysical therapyPedagogyPolitical science

Abstract

fetched live from OpenAlex

1. The management of pain frequently requires healthcare professionals (HCPs) to work together; thus, educational preparation should afford them opportunities to learn about the management of pain together. 2. Survey data suggest that most HCPs' curricula do not provide opportunities for learners to come together to learn about pain and understand their professional roles. 3. Despite the growth of published evaluations of interprofessional education (IPE) and pain, the ability to draw firm conclusions has been hampered by the lack of methodological heterogeneity across studies. 4. New directions in IPE and pain include innovative pedagogical approaches, web-based learning, standardised patients and simulated learning. 5. Harnessing the political agenda can offer a valuable opportunity to raise the profile and prominence of pain education for HCPs.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.376
Teacher spread0.352 · 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 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

Citations23
Published2012
Admission routes1
Has abstractyes

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