The ‘wicked problem’ of telerehabilitation: Considerations for planning the way forward
Bibliographic record
Abstract
Telerehabilitation offers great promise to improved access to rehabilitation care. The rising use of technology, the increased expansion of data networks worldwide, and the growing confidence and interest of the general population to incorporate technology into their day-to-day lives via the Internet, smartphones and wearables provide fertile ground for many rehabilitation interventions. Despite this opportunity, telerehabilitation is not integrated into existing health care systems today. Most research is focused on the efficacy of the intervention without addressing the complexity of introducing a system of care that is starkly different from the current health care system in most countries. As such, implementation of telerehabilitation may be considered a ‘wicked problem’ in that it is extremely complex and challenging situation that is intricately linked with the social, economic and political contexts. This paper discusses telerehabilitation implementation while considering the intervention, patient, and health care system contexts in which it occurs.
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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 | 0.111 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.034 | 0.047 |
| Open science | 0.012 | 0.025 |
| Research integrity | 0.028 | 0.040 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".