Telelearning in a partnership between a university faculty and a regional health authority: Benefits, challenges and strategies
Bibliographic record
Abstract
The University of Calgary's Faculty of Medicine and the Calgary Regional Health Authority understand that telehealth is an evolving field requiring both academic enquiry and operational readiness. Both parties are committed to quality educational programmes--the Faculty through its commitment to excellence and the Authority with its charge to maintain and enhance such programmes. There are shared applications, multi-learner user groups, shared strategies to overcome distances and shared infrastructure--technologies, communication pathways and resources. Having embarked on a joint telelearning venture, we have learned a number of lessons. Central to progress has been an appreciation and respect for unique mandates, a spirit of trust and flexibility, an agreement on a set of principles, ongoing communication between and participation from the users and, at times, redirection. Questions being answered include the following. How well is this collaborative model working? How functional is it at this time of health reform and restructuring? Can one meet complementary telelearning goals within a faculty-health authority relationship? These all have implications for future success.
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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".