Telesupervision Benefits for Placements: Allied Health Students’ and Supervisors’ Perceptions
Bibliographic record
Abstract
Telesupervision (TS) uses Information and Communication Technology (ICT) for communication between university-based staff, clinical supervisors and students undertaking placements in the presence or absence of a clinical supervisor onsite. Despite examples of successful implementation (Carlin 2012, Chipchase et al. 2014, Dudding and Justice 2004, Hall 2013) there has been minimal uptake of TS in allied health. This study investigated students’ and clinical educators’ perceptions of the potential benefits and barriers of TS using readily accessible ICT during placements. During 2014-2015, telesupervision/telesupport was provided to a total of 54 Undergraduate and Graduate Entry Masters students from Speech Language Pathology (SLP), Occupational therapy (OT) and Physical therapy (PT) programs at one Australian and two Canadian universities and Exercise Physiology (EP) students at the Australian university. After receipt of TS, 39 students completed an online survey. Nine participating university-based clinical education coordinators (CECs) were interviewed about their experiences. Survey data were analysed using descriptive statistics and interview data were analysed using thematic analysis. Students valued regular TS contact/communication with their CEC to discuss challenges that arose during their placements. CECs believed students benefitted from the opportunities to discuss their placement experiences through TS sessions used for direct supervision and/or for complementing onsite supervision. Students used TS sessions to debrief and reflect on their placement experiences. CECs gained a better understanding of the students’ placement experiences. TS has the potential to develop greater connection between students and CECs and enhance student and supervisor experience of clinical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".