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Record W2133675567 · doi:10.1037/prj0000093

Examining strategies to improve accelerometer compliance for individuals living with schizophrenia.

2014· article· en· W2133675567 on OpenAlexaff
Paul Gorczynski, Guy Faulkner, Tony Cohn, Gary Remington

Bibliographic record

VenuePsychiatric Rehabilitation Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAccelerometerSchizophrenia (object-oriented programming)Compliance (psychology)Intervention (counseling)PopulationPhonePhysical medicine and rehabilitationPsychologyMobile phoneApplied psychologyMedicinePhysical therapyPsychiatryComputer scienceSocial psychologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: This study examined the feasibility and effect of 2 investigator-based and 2 participant-based strategies on accelerometer wear time in individuals living with schizophrenia in order to improve accelerometry compliance. METHOD: Four adults with schizophrenia were asked to wear an accelerometer for 1 week during the baseline, intervention, and follow-up phases of a study that evaluated exercise counseling. To encourage participants to wear their accelerometers, investigators modeled proper accelerometer use, provided verbal and written instructions, and placed reminder phone calls. Participants were also given wear time logs and reminder magnets. RESULTS: All participants wore their accelerometers for the required amount of time during the study. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Researchers should use multiple techniques to help ensure compliance. Research is needed to identify the most effective combination of strategies for this population.

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)0.001
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 score0.453
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.049
GPT teacher head0.340
Teacher spread0.291 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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