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Record W19608961 · doi:10.2105/ajph.2008.144832

A Comparison of Exergaming Interfaces for Use in Rehabilitation Programs and Research

2012· article· en· W19608961 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLoading... · 2012
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsMount Royal UniversityUniversity of Calgary
Fundersnot available
KeywordsVideo gameFocus (optics)Human–computer interactionMotion (physics)MultimediaComputer scienceInterface (matter)RehabilitationPhysical activityMotion sensorsPhysical medicine and rehabilitationPhysical therapyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Respondent-driven sampling is especially useful for reaching hidden populations and is increasingly used internationally in public health research, particularly on HIV. Respondent-driven sampling involves peer recruitment and has a dual-incentive structure: both recruiters and their peer recruits are paid. Recent literature focusing on the ethical dimensions of this method in the US context has identified integral safeguards that protect against ethical violations. We analyzed a study of 3 groups in Lebanon who are at risk for HIV (injection drug users, men who have sex with men, female sex workers) and the ethical issues that arose. More explicit attention should be given to ethical issues involved in research implementing respondent-driven sampling of at-risk populations in developing countries, where ethical review mechanisms may be weak.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.213
GPT teacher head0.492
Teacher spread0.280 · 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