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Record W4310959989 · doi:10.1177/07334648221144022

“It’s better than nothing, but I do not find it to be ideal”: Older adults’ experience of TeleRehab during the first COVID-19 lockdown

2022· article· en· W4310959989 on OpenAlexafffund
Shlomit Rotenberg, Julie S. Oreper, Yael Bar, Naomi Davids-Brumer, Deirdre Dawson

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsBaycrest HospitalToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPsychologyTheme (computing)Context (archaeology)Thematic analysisIntervention (counseling)PreferenceNothingApplied psychologySocial psychologyGerontologyQualitative researchDevelopmental psychologyMedicineSociology

Abstract

fetched live from OpenAlex

This qualitative study used descriptive thematic analysis to explore the experiences of 16 older adults (age: 71 ± 6.4) who transitioned from an in-person to telerehabilitation (TeleRehab) group intervention in March 2020. We found the following themes: (1A) Technology Use, describing challenges and need for support; and (1B) Technology Self-Efficacy, describing how technological ability was attributed to past-experience and/or age. Four themes described the intervention experience. First, "Not The Same, But Better Than Nothing" (2A), reflected a preference for in-person intervention. Specifically, in-person training provided a better social experience (theme 2B), and stronger accountability, although the content was well delivered in both modalities (theme 2C). Contextual factors (theme 2D) that played a role were ease of commute, especially important during the winter, and the context of the lockdown, that positioned the TeleRehab intervention as a meaningful social activity. However, sensory impairments, and/or distractions in the home diminished the TeleRehab experience.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.038
GPT teacher head0.343
Teacher spread0.305 · 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 teacher head, not a consensus.

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

Citations3
Published2022
Admission routes2
Has abstractyes

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