Development and initial psychometric evaluation of a scale measuring factors related to motivation for exercise.
Bibliographic record
Abstract
Background. This thesis was guided by a series of research objectives, which were to: examine the literature to identify motivational factors for regular vigorous physical activity; draft an instrument that measures these motivational factors and pre-test it; conduct additional testing into how the items and instruction sets of the instrument were being understood; conduct an initial evaluation of the content, criterion-related, and construct validity of the instrument; and to assess the internal consistency and test-retest reliability of the instrument. Methods. (1) Instrument: Following scale development work, the final scale comprised 82-items that were scored on a five-point type scale, measuring the following fifteen sub-scales: Affect, Attitude, Affiliation, Barriers, Goals, Outcome expectancy, personal Normative Beliefs (PNB), Rewards, Self-determination, Self-efficacy, Self-evaluation, Self-presentation, Social comparison, Social support, Time. (2) Procedure: This cross-sectional study was conducted using a non-probability sample recruited from several private and governmental worksites or organizations. (Abstract shortened by UMI.)
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".