MétaCan
Menu
Back to cohort
Record W4303981198 · doi:10.3390/s22197500

Caregivers’ Profiles Based on the Canadian Occupational Performance Measure for the Adoption of Assistive Technologies

2022· article· en· W4303981198 on OpenAlexaboutno aff
Francesco Della Gatta, Elisa Fabrizi, Franco Giubilei, D Grau-Sevilla, Carmen Moret‐Tatay

Bibliographic record

VenueSensors · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionIdentification (biology)Psychological interventionPsychologyHierarchyApplied psychologyMeasure (data warehouse)Sample (material)Cluster (spacecraft)Assistive technologyHierarchical clusteringMultilevel modelComputer scienceHuman–computer interactionCluster analysisData miningArtificial intelligencePsychiatryMachine learning

Abstract

fetched live from OpenAlex

The COPM (Canadian Occupational Performance Measure) is a tool that is based on the identification of self-perceived performance and satisfaction problems in the performance of occupations, allowing the creation of a hierarchy in the order of the interventions to be carried out, and speeding up the identification of the necessary AT (Assistive Technologies). Given the importance of the caregiver's perception about their own performance in the design of AT, this research examines the caregiver's profile through the COPM. A sample of 40 caregivers volunteered to participate in the study. A cluster analysis was carried out on the COPM scores. Two caregiver profiles were found in relation to the COPM measure, one with low scores on performance and satisfaction and another with high scores on both of these two variables. The main predictor was found to be the self-perception of performance. The structure was replicated through a hierarchical cluster analysis, where the role of caregivers was of interest. These results are relevant on both a theoretical and practical level.

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)0.006
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 score0.850
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.087
GPT teacher head0.363
Teacher spread0.276 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueSensorsSame topicAssistive Technology in Communication and MobilityFrench-language works237,207