Web‐based diaries − windows to student internship feedback experiences
Bibliographic record
Abstract
Context and setting Clinical internships in physiotherapy are a valued learning experience providing students with authentic problems and complex challenges. Students need to reflect on their experiences during the internship to enhance their learning. Feedback experiences are a powerful but largely neglected component of learning in clinical settings. Why the idea was necessary The web-based diary program was created to promote student reflection about their internship experiences and to provide academic faculty with a window to the students' experiences in the clinical environment. Reflection is recognised as a vital component of learning and professional practice but may be neglected by students in demanding clinical internships. There are geographical and system barriers between the clinical and academic settings which prevent academic faculty from fully understanding the students' experiences during internship. What was done Twenty-one senior physiotherapy students in advanced outpatient musculoskeletal clinical internships volunteered to submit bi-weekly web-based diary entries during their 5-week internship. Students used diary software with embedded guiding questions to write about the feedback they received from their clinical instructors. The software developed by Atsoft Inc., specifically for this study allowed faculty and researchers to interact with students while maintaining confidentiality and anonymity on a secure web site. Evaluation of results and impact Over a 5-week period, 210 feedback interactions yielding 229 pages of diary entries were captured. Diary entries were analysed for emerging themes about feedback. Analysis yielded 3 key findings about feedback received by students in the clinical setting. Students perceived the need for a clear and explicit delivery process. From the diaries a feedback process was developed which was later presented by the students to the clinical instructors in a workshop. The second theme focused on the conflict of openness in the clinical environment. Students referred frequently to ‘openness’ and used the word in a variety of contexts. They expressed an appreciation of the value of openness on the part of the instructor about their performance but admitted that they felt reluctant to disclose their own knowledge deficits or to acknowledge that they had made a mistake. They recognised the inherent danger that potentially existed in admitting or denying errors and knowledge deficiencies. Finally, the relationship between the clinical instructor and student was found to be a powerful determinant of whether the students described their clinical internships as a positive or negative experience. Students gained valuable insights by reflecting about their feedback experiences and became more active participants in the process of ensuring that the internship met their learning needs. Academic faculty gained a deeper understanding of the concerns and issues faced by students during internship which can be addressed through the program curriculum. Web-based diaries improved student reflection and allowed faculty to view and understand student experiences without temporal or geographical constraints. This software has the potential to encourage students to reflect on a variety of topics and allows faculty to identify and provide real-time intervention regarding issues that might otherwise be overlooked during clinical internships.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".