Bibliographic record
Abstract
Telehealth enables the delivery of specialized health care to patients living in isolated and remote regions. The purpose of this analysis is to determine the current uptake of teleoncology in mainland British Columbia. Patient appointment data was extracted from the Cancer Agency Information System (CAIS) for the 2009 calendar year. Three types of practitioners used teleoncology in 2009: Medical Oncologists, Genetic Counsellors and Medical Geneticists. In total, 712 telehealth encounters were conducted; Medical Oncologists conducted 595 encounters (83.6%), Genetic Counsellors conducted 112 encounters (15.7%) and Medical Geneticists conducted 5 encounters (0.7%). The most common oncology appointments were Gastro-Intestinal (11.4%) and Lymphoma (11.0%) follow-up appointments with a Medical Oncologist. Telehealth encounters were conducted by 46 individual health care providers however, a single Medical Oncologist conducted 418 encounters and this accounts for more than half (58.7%) of all telehealth appointments in 2009. Radiation Oncologists on the mainland up to this point are not using the technology. The Local Health Areas with the highest number of oncology telehealth appointments were: Kamloops: 203 encounters (34.1%), Penticton: 84 encounters (14.1%), Cranbrook: 58 encounters (9.7%) and the Southern Okanagan: 33 encounter (5.5%). Use of telehealth in rural and remote areas of BC is limited and there is significant room for growth. Further research will be required to identify barriers and restrictions to the use of telehealth in order to increase teleoncology adoption in British Columbia.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".