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Record W2594627988 · doi:10.1080/02703181.2017.1283656

Development of an Assistive Technology Intervention for Community Older Adults

2017· article· en· W2594627988 on OpenAlexaff
Elsa M. Orellano-Colón, Frances M. Morales, Zahira Sotelo, Nilkenid Picado, Edgardo J. Castro, Mayra Torres, Marta Rivero‐Méndez, Nelson Varas, Jeffrey W. Jutai

Bibliographic record

VenuePhysical & Occupational Therapy In Geriatrics · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of Ottawa
FundersNational Institute on Drug AbuseNational Institute on Minority Health and Health DisparitiesAmerican Psychology-Law Society
KeywordsIntervention (counseling)Assistive technologyFocus groupGerontologyPsychologyQualitative researchIndependent livingApplied psychologyNursingMedicineComputer scienceHuman–computer interactionSociology

Abstract

fetched live from OpenAlex

AIM: To explore the use ofthe Ecological Validity Model as a guiding framework in the provision of a culturally-sensitive assistive technology (AT) intervention for community older people. METHODS: Twenty-seven Hispanic adultsaged 70 years and older, and four individuals with expertisein AT participated in a concurrent nested mixed method study where the quantitative method (content validity ratio exercise) was embedded in the dominant qualitative method (focus groups). RESULTS: Findings informedthe development of the Assistive Technology Life Enhancement Program (ATLEP); an intervention consisting of seven modules addressing AT devices with culturally sensitive elements. CONCLUSIONS: The Ecological Validity Model, as well as, the input from older adults were both effective methodological strategies in tailoring the ATLEP intervention to the needs and circumstances of community-living older people living in Puerto Rico.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.135
GPT teacher head0.498
Teacher spread0.363 · 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 designNon-randomized trial
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

Citations8
Published2017
Admission routes1
Has abstractyes

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Same venuePhysical & Occupational Therapy In GeriatricsSame topicAssistive Technology in Communication and MobilityFrench-language works237,207