MétaCan
Menu
Back to cohort
Record W3017162615 · doi:10.5539/ies.v13n5p72

Transfer of Learning for Evidence-Based Practice in Psychology

2020· article· en· W3017162615 on OpenAlexvenueno aff
Ana Lucía Jiménez Pérez, Eunice Vargas‐Contreras

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychology Research and Bibliometrics
Canadian institutionsnot available
FundersUniversidad Autónoma de Baja CaliforniaUniversidad Nacional Autónoma de MéxicoUniversidad Autónoma de Aguascalientes
KeywordsPsychologyIntervention (counseling)Observational studyTransfer of trainingMedical educationEvidence-based practiceMathematics educationPedagogyApplied psychologyMedicineCognitive psychology

Abstract

fetched live from OpenAlex

The aim of this paper is to assess the transfer of learning of students from a master’s degree in psychology through observation of their skills in information assessment and intervention. The participants were 10 incoming students of a professional-aimed master’s program in psychology offered in a Mexican public university. They carried out two types of tasks: a) review of a scientific text and b) interaction in intervention with clients, where the participants were evaluated by observational strategies. The results reflect zero relation between the level of skill displayed by most students in their information assessment skills and their intervention skills, which could suggest a low probability to conduct evidence-based practice. The data presented provide information for the design of graduate programs in order to foster the transfer of learning of these skills, which is essential for evidence-based practice.

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)0.124
metaresearch head score (Gemma)0.422
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 score0.124
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1240.422
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0020.004
Scholarly communication0.0060.007
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.402
GPT teacher head0.603
Teacher spread0.201 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Education StudiesSame topicPsychology Research and BibliometricsFrench-language works237,207