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APLICABILIDAD Y CONFIABILIDAD DEL INSTRUMENTO DE EVALUACIÓN DE DESEMPEÑO HERNÁNDEZ NEUMAN EN EL CONTEXTO COLOMBIANO

2023· article· es· W4386879631 on OpenAlexaff
Nubia Constanza Zapata Parada, María Fernanda Cámaro, Yennifer Fernández Rey, Yulieth Nataly Bautista

Bibliographic record

VenueLÍNEA IMAGINARIA · 2023
Typearticle
Languagees
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsHumanitiesContext (archaeology)Occupational therapyPsychologyPhilosophyGeography

Abstract

fetched live from OpenAlex

En la actualidad, las actividades de la vida diaria básicas e instrumentales están definidas en el Marco para el Trabajo de la Terapia Ocupacional de la Asociación Americana de Terapia Ocupacional (AOTA). Este marco reconoce ocho áreas ocupacionales, entre las cuales se encuentran las Actividades de la Vida Diaria Instrumentales (AVDI), las cuales son fundamentales para determinar el nivel de participación de una persona en su contexto. De tal forma la evaluación del desempeño de las actividades instrumentales de la vida diaria se establece como herramienta crucial en el ámbito de la salud al permitir cuantificar científicamente el desempeño y participación, identificando actividades y áreas para la intervención, a fin de establecer metas y estrategias de desempeño ocupacional. En el cual se prevé profundizar en el estudio y análisis del Instrumento de Evaluación de Desempeño en AVDI, diseñado por Hernández Neumann, para comprender su importancia, alcance y contribución en el ámbito de la evaluación del desempeño. Se examinará cómo este instrumento, basado en una metodología integral y multidimensional, permite evaluar tanto aspectos cuantitativos como cualitativos del desempeño de las personas, brindando una visión holística de su participación en relación al contexto donde interactúa.

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)0.035
metaresearch head score (Gemma)0.097
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.097
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0030.004
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.090
GPT teacher head0.534
Teacher spread0.444 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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