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What Do Confidence Items Measure in the Physical Activity Domain?<sup>1</sup>

2007· article· en· W1997609925 on OpenAlexaff
Ryan E. Rhodes, Chris M. Blanchard

Bibliographic record

VenueJournal of Applied Social Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsDalhousie UniversityUniversity of Victoria
Fundersnot available
KeywordsPsychologyConfidence intervalListing (finance)Self-confidencePhysical activityDomain (mathematical analysis)Measure (data warehouse)Social psychologyStatisticsMathematicsComputer scienceMedicineData mining

Abstract

fetched live from OpenAlex

The study's purpose was to examine the measurement domain of confidence items used in physical activity research. We hypothesized that confidence items, including a phrase to hold motivation constant, would differ from standard confidence items. Participants (N = 248 students) completed confidence items, a thought‐listing procedure, and a 2‐week self‐report of physical activity. Results showed that confidence items with motivation held constant loaded exclusively on one factor, but standard confidence items were factor complex with intention. Correlations with physical activity intention and behavior were larger for confidence items than confidence items with motivation held constant. Finally, the thought‐listing procedure identified that 3 of the 7 reasons for answering confidence items were outside the intended measurement domain of self‐efficacy.

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)0.005
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.084
GPT teacher head0.456
Teacher spread0.372 · 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 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

Citations66
Published2007
Admission routes1
Has abstractyes

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