Self-regulated learning in a field-based setting: Metacognitive and motivational strategies used by occupational therapy fieldwork students to develop clinical competence (Doctoral Dissertation, University of Connecticut, 2005)
Bibliographic record
Abstract
Occupational therapy students often complete their fieldwork education lacking competencies needed for practice. The length of fieldwork has remained constant over the years, while the nature of practice is more complex. This study identified self-regulated learning strategies that help occupational therapy fieldwork students develop clinical competence within the current fieldwork requirement. ^ Self-regulated learning has a positive impact on academic performance. A model of self-regulated fieldwork learning, based on research in academic settings, including contextually relevant metacognitive and motivational variables, provided the theoretical rationale for this study. Qualitative research methods were selected, since the nature of self-regulated learning in field-based settings had not been described in the literature. ^ Data were collected from 12 occupational therapy students engaged in fulltime fieldwork. Nine participants were identified as high performing students (above average or outstanding) and their data were used to describe effective self-regulated fieldwork learning. Two participants were identified as below average students and their data served as a cross comparison to better understand how themes describing self-regulated learning strategies of effective learners were qualified by varied conditions. ^ Four primary themes emerged from the data that described self-regulated fieldwork learning used to develop clinical competence. Effective occupational therapy fieldwork students. (1) used metacognitive control to create and expand their own mental models for clinical competence, (2) demonstrated self-determined motivation that supported the use of metacognitive strategies, (3) took active steps to develop and maintain a productive learning alliance with their supervisors, (4) examined the person-environment interface and were strategic in selecting self-regulated learning strategies. ^ Results of the study were consistent with theories of self-regulated learning based on research in academic settings, but the data suggested that fieldwork learners used additional strategies specific to the fieldwork setting. Effective fieldwork students used self-regulatory learning strategies designed to simultaneously promote learning while ensuring effective patient care. Elaborating on self-regulated learning strategies used in academic settings to better meet the demands of the clinical sites may facilitate students' transition from fieldwork learning to lifelong learning in the workplace. ^
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".