WHICH MOTIVATIONAL BEHAVIORS IMPACT SUCCESS IN A FOUNDATIONAL ANATOMY COURSE FOR ENTRY DOCTOR OF PHYSICAL THERAPY STUDENTS?
Bibliographic record
Abstract
Objectives: The motivational behavior of self-efficacy for learning and performance was correlated with academic success in Doctor of Physical Therapy (DPT) students taking clinical anatomy, the first foundational course in the program. Students’ motivation strategies have been reported to be important factors in academic success, however, these strategies have not been investigated in DPT students. Therefore, the purpose of this study was to determine if course grade in clinical anatomy was correlated with the motivation subscales of the Motivated Strategies for Learning Questionnaire (MSLQ). Materials and Methods: The MSLQ was administered to thirty-three first-year DPT students who consented to participate in the study. Correlation (Pearson r zero order) between the subscales and final course grade in clinical anatomy were determined. Results: Self-efficacy for learning and performance was correlated with course grade (r(31) = .44, p < .05), while intrinsic and extrinsic goal orientation, task value, control of learning beliefs, and test anxiety, were poorly correlated. Conclusions: The results of the current study, indicating that self-efficacy for learning and performance is correlated with academic success, could be utilized in DPT programs to broaden admission processes, and aid in the development of remedial curricular and teaching strategies to support students identified with poor self-efficacy for learning and performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".