The Motivation Competencies That Count Most: An Online International Study
Bibliographic record
Abstract
Background: With an online sample of 8,349 people from 123 countries (74.9% from the U.S., Canada, and India), a new test was used to rank eight motivation-related competencies according to how well they predicted desirable, self-reported outcomes. Each of the competencies was derived from empirical studies showing that such competencies were associated with higher levels of motivation. The competencies were: Maintains Healthy Lifestyle, Makes Commitments, Manages Environment, Manages Rewards, Manages Stress, Manages Thoughts, Monitors Behavior, and Sets Goals. Objective: The study was conducted to identify and prioritize competencies that are associated with higher levels of motivation. Methods: A “concurrent study design” was used to assess predictive validity, which was suggested by a strong association between test scores and self-reported answers to criterion questions about levels of motivation, life satisfaction, and professional success. Regression analyses were conducted to prioritize the competencies. Demographic analyses were also conducted. Results: The findings support the value of motivation training; test scores were higher for people who had received such training and were positively correlated with the number of training hours accrued. Effects were found for education, race and age, but no male/female difference was found. Regression analyses pointed to the importance of two of the eight competencies in particular: Sets Goals and Manages Thoughts. Conclusion: The study supports the view that motivation competencies can be measured and trained and that they are predictive of desirable motivational outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".