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Record W4225140881 · doi:10.1177/15394492221091266

Feasibility of an Online Langerian Mindfulness Program for Stroke Survivors and Caregivers

2022· article· en· W4225140881 on OpenAlexfundno aff
Marika Demers, Francesco Pagnini, Deborah Phillips, Brianna Chang, Carolee J. Winstein, Ellen J. Langer

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéSouthern California Clinical and Translational Science Institute
KeywordsMindfulnessIntervention (counseling)UsabilityClinical psychologyStroke (engine)PopulationMedicinePsychologyPsychological interventionPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Mindfulness is promising for individuals with neurological disorders and their caregivers to improve psychological well-being. The potential application of a Langerian mindfulness intervention, focused on attention to variability, however, is still unknown. The objective of the study was to determine the feasibility (usability, satisfaction, and potential effectiveness on psychological well-being) of an online mindfulness intervention for stroke survivors and caregivers. Using mixed methods, 11 stroke survivors and three caregivers participated in a 3-week, online, Langerian mindfulness intervention. A semi-structured interview assessed the intervention's usability and gathered feedback. Self-reported measures about psychological well-being were documented remotely 3 times (preintervention, postintervention, and 1-month follow-up). Qualitatively, participants were highly satisfied with the intervention and reported subjective benefits, but the usability of the online platform was poor. None of the self-reported measures changed over time. This study provided evidence of feasibility of an online Langerian mindfulness intervention in a new population: stroke survivors and caregivers.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.389
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.194
GPT teacher head0.477
Teacher spread0.283 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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