Emotional-Social Intelligence of Physical Therapy Students during the Initial Academic Component of Their First Professional Year
Bibliographic record
Abstract
Purpose: To describe and compare the development of emotional-social intelligence (ESI) of physical therapy students from a traditional education program and a problem-based learning (PBL) program during the initial academic component of their first professional year of studies. Methods: At the beginning of their first professional year (time 1), sixty students (39 from the traditional program, 21 from the PBL program) completed the Bar-On Emotional Quotient Inventory Short (EQ-i:S) for ESI. The EQ-i:S provides a total score and five subscale scores (Intrapersonal, Interpersonal, Stress Management, Adaptability, and General Mood). Higher scores mean higher levels of ESI. The students completed the EQ-i:S again at the end of their first academic year, just prior to commencing their first full-time clinical placement (time 2). Results: A two-way ANOVA with repeated measures (group versus time) revealed significant group by time interaction effects (p<.001) for the total EQ-i:S score and the intrapersonal, Stress Management and General Mood subscale scores. This interaction was a result of a decrease in scores for students from the traditional education program [Total Score: 105.0 (9.3) to 100.0 (11.3)], and an increase for those from the PBL program [Total Score: 98.3 (11.4) to 101.9 (13.1)] from time 1 to time 2. Conclusion: Although the observed changes in ESI were small in both groups, the patterns of change were different in students from traditional and PBL programs. More research is required to determine the reason for these differences.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".