Exploring the meta-motivational strategies utilized by medical students in Jordan: an exploratory study
Bibliographic record
Abstract
BACKGROUND: Meta-motivational strategies refer to the ability to monitor and adapt one's motivational state to accomplish certain goals and are highly significant in medical students due to their unique educational environment. The utilization of such strategies has not been previously studied in Jordanian medical students. METHODS: A cross-sectional design surveyed 409 students using the Meta-Motivational Strategies in Medical Students Questionnaire (MSMQ), assessing seven domains: regulation of value, environmental structuring, relatedness, promotional/preventional situational awareness, situational interest, and self-consequencing. RESULTS: Key findings revealed regulation of value and environmental structuring as dominant strategies, aligning with societal emphasis on education and adaptive responses to academic rigor. Male students scored significantly higher in regulation of relatedness (MD: -0.79, p < 0.05). Students living alone demonstrated stronger environmental structuring (MD: 1.16, p < 0.05) and self-consequencing (MD: 0.68, p = 0.024). Students who chose to enroll in medicine autonomously scored higher across most strategies (p < 0.05). Students who participated in research activities exhibited greater regulation of value and situational interest (p < 0.05). GPA disparities highlighted that high achievers (Excellent GPA) scored higher in regulation of value and environmental structuring (p < 0.05). No differences emerged between first-generation and non-first-generation students. CONCLUSION: These findings emphasize cultural, institutional, and psychological influences on meta-motivation; by bridging these gaps, educators can develop targeted interventions to foster adaptive meta-motivational strategies, ultimately supporting student well-being and success in medicine.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".