MétaCan
Menu
Back to cohort
Record W2037091657 · doi:10.11157/fohpe.v16i2.74

Interprofessional preceptor and preceptee educational programing: An interdisciplinary needs assessment

2015· article· en· W2037091657 on OpenAlexaff
Elizabeth Anne Kinsella, Anne Bossers, Karen Jenkins, Sandra Hobson, Ann MacPhail, Susan Schurr, Taslim Moosa, Karen Ferguson

Bibliographic record

VenueFocus on Health Professional Education A Multi-Professional Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsFanshawe CollegeWestern University
Fundersnot available
KeywordsPreceptorMedical educationRelevance (law)Resource (disambiguation)Needs assessmentMedicinePsychologyNursingPedagogySociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This paper presents the results of a needs assessment undertaken by an interdisciplinary team concerned with developing an interprofessional preceptor and preceptee education program for health professionals and students. The study draws on a pragmatic philosophical perspective, to undertake what Robson (1993) referred to as “real world research”, with the aim of applying the results for practical purposes. The objective of the needs assessment was to identify which content areas were considered to be of greatest educational value to an interdisciplinary range of health professional preceptors and students. In addition, the needs assessment sought to ascertain whether preceptors and students would find such an education resource useful, and if they would use a web-based electronic resource, and to identify preferred formats of potential educational modules. The needs assessment involved three phases: a literature review, an environmental scan of available preceptor/preceptee education programs and the design and implementation of a preceptor/preceptee survey. The results of the study point to content areas in education and design that hold relevance for both preceptors and preceptees. The findings hold significance for others concerned with preceptor and student preparation, and informed the development of an open access, online interprofessional educational program (Bossers et al., 2007).

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0060.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.075
GPT teacher head0.554
Teacher spread0.479 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueFocus on Health Professional Education A Multi-Professional JournalSame topicInterprofessional Education and CollaborationFrench-language works237,207