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Record W2134437662 · doi:10.46743/1540-580x/2011.1351

Meeting the Clinical Education Needs of Community-Based Preceptors: An Environmental Scan to Identify Format and Content for a New Web-Based Resource

2011· article· en· W2134437662 on OpenAlexafffundabout
Rosemin Kassam, Elizabeth MacLeod, John B. Collins, Glymnis Tidball, Donna Dryan, Lois Neufeld, Mona Kwong

Bibliographic record

VenueInternet Journal of Allied Health Sciences and Practice · 2011
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsPenticton Regional HospitalSt. Paul's HospitalUniversity of British Columbia
FundersMinistry of Health, British Columbia
KeywordsPreceptorResource (disambiguation)Web resourceWorld Wide WebMedical educationWeb applicationEducational resourcesComputer scienceKnowledge managementMedicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

This study gathered world-wide information about web-based clinical teaching resources, identified gaps in these resources, and analyzed preceptor clinical education needs as first steps in creating a series of web-based clinical education modules. In addition to an environmental scan of web-based resources, a needs assessment survey was created, distributed, and analyzed. Participants in the survey, representing ten healthcare professions throughout British Columbia, Canada, identified the content that would be most relevant to them and the optimal length of web-based modules. The study identified 15 web-based clinical education topics common across four English-speaking countries and linked them to 31 province-wide learning needs surveyed across ten categories of allied health professionals. The results indicated a strong interest among preceptors in using a web-based resource and provided initial groundwork about the topical content and structural formats for the next phase of the project which will be to develop and evaluate a series of web-based clinical teaching modules.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.273
GPT teacher head0.540
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2011
Admission routes3
Has abstractyes

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