Learning and Study Strategies of Students in the First Year of an Entry-Level Physical Therapist Program
Bibliographic record
Abstract
INTRODUCTION: The learning and study strategies of entry-level physical therapist (PT) students may not be as effective as those needed for success in an entry-level PT education program. The Learning and Study Strategies Inventory, third edition (LASSI) is a reliable tool to assess learning and study strategies. The purpose of this study was to assess the learning and study strategies of first-year PT students and if the strategies change over the first year. REVIEW OF LITERATURE: There is little research on using the LASSI with PT students; however, the LASSI has been used with other health care professional students. SUBJECTS: The participants (n = 211) were from 5 cohorts of PT students in their first year of an entry-level PT education program. METHODS: In the first week of the curriculum the students took the LASSI in class. The results were released individually to each student. No intervention was provided. At the end of the first year, the students retook the LASSI. Paired samples t-tests were run to determine whether the 10 subtest mean percentile scores changed significantly from baseline to follow-up and how they compared to established LASSI benchmarks. RESULTS: Six subtest scores, Anxiety, Attitude, Concentration, Information Processing, Selecting Main Ideas, and Test Strategies, showed significant changes (P ≤ .005) from baseline to follow-up. However, all subtest averages fell below the 75th percentile mark, which is reported as the threshold requiring reflection to improve skills for learning and study strategies. DISCUSSION AND CONCLUSION: Students, on average, may not have adequate learning and study strategies when they start an entry-level PT education program. The LASSI may be an effective tool to focus resources in a timely and proactive manner for those students who may need them. Determining the resources needed earlier may decrease the need for later remediation, attrition, or licensure examination failures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".