Measuring implicit attitudes for exercise in older adults with chronic lung diseases: A feasibility study
Bibliographic record
Abstract
Purpose: This research aims to determine the feasibility of the Go/No-go Association Task (GNAT) as an effective measurement of implicit attitudes for exercise in older adults with chronic lung disease. The GNAT is a paced computer response task, with typical response deadlines between 500 and 1000 milliseconds (ms), which may be too fast for older adults. Method: Sixteen regular exercisers who graduated from a pulmonary rehabilitation program (Mage=72) completed the GNAT (n=8 at 1000ms response deadline, n=8 at 1500ms response deadline) and a questionnaire assessing outcome expectations for exercise. Results: Examination of error rates and response times found that participants with the 1000ms response deadline timed out more frequently and were less able to inhibit responses on the no-go trials (where correct response is no response) than participants with the 1500ms response deadline. A repeated measures ANOVA showed a trend towards positive implicit attitudes for exercise for the 1500ms response deadline, p = .06, ?p2= .41, and no effect of implicit attitudes for the 1000ms response deadline, p = .46, ?p2= .07. Implicit attitudes were correlated to the likelihood and desirability of enjoyment, health, and appearance outcomes (r's .30 to .77). Conclusion: Less timing out during response periods, better response inhibition, and generation of viable implicit attitude scores suggests the 1500ms response deadline is more suitable than the 1000ms response deadline in older people with chronic lung diseases. Planned future research will compare these results to a healthy aged-matched sample to discern age-related from disease-related differences in response times.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".