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Record W2002601212 · doi:10.1080/02701960.2012.679369

The Longitudinal Elderly Person Shadowing Program: Outcomes From an Interprofessional Senior Partner Mentoring Program

2012· article· en· W2002601212 on OpenAlexaffabout
Jenny Basran, Vanina Dal Bello‐Haas, Doreen M.C. Walker, Peggy MacLeod, Bev Allen, Marcel D’Eon, Meredith McKague, Nicola S. Chopin, Krista Trinder

Bibliographic record

VenueGerontology & Geriatrics Education · 2012
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSemantic differentialInterprofessional educationPerceptionScale (ratio)PharmacyGerontologyPsychologyMedical educationNursingMedicineHealth careSocial psychology

Abstract

fetched live from OpenAlex

The University of Saskatchewan's Longitudinal Elderly Person Shadowing (LEPS) is an interprofessional senior mentors program (SMP) where teams of undergraduate students in their first year of medicine, pharmacy, and physiotherapy; 2nd year of nutrition; 3rd year nursing; and 4th year social work partner with community-dwelling older adults. Existing literature on SMPs provides little information on the sustainability of attitudinal changes toward older adults or changes in interprofessional attitudes. LEPS students completed Polizzi's Aging Semantic Differential and the Interdisciplinary Education Perception Scale. Perceptions of older men and women improved significantly and changes were sustained after one year. However, few changes were seen in interprofessional attitudes.

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)0.002
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.461
Teacher spread0.361 · 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

Citations40
Published2012
Admission routes2
Has abstractyes

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