Managing Changes in First Nations’ Health Care Needs: Is Telehealth the Answer?
Bibliographic record
Abstract
The health care needs of First Nations are changing. Chronic diseases now account for most hospital admissions, partially as a result of underinvestment in primary health care. This situation results in an unnecessary reliance on secondary and tertiary care, at a must higher cost to the provincial health care systems, and human cost to First Nations themselves. Telehealth is being promoted as a possible solution. This remains under-researched. While cost savings related to transportation have been documented, researchers have yet to tackle potential efficiencies across the federal/provincial health system divide. Les besoins des Premières nations en matières de services de santé sont en transition. Les maladies chroniques constituent la majorité des admissions dans les hôpitaux, en partie due à un manque d’investissement dans les soins de santé primaires. Cette situation résulte en une dépendance envers les services de santé secondaires et tertiaires, et engendre des coûts additionnels pour les systèmes de santé provinciaux, ainsi que des coûts humains considérables pour les Premières nations. Télésanté est maintenant promu comme une solution possible. Alors que des économies en terme de transport ont été documentées, la recherche ne s’est pas penchée sur les efficacités potentielles à réaliser à travers les systèmes fédéraux et provinciaux.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Commentary on telehealth for First Nations health care needs; notes that the topic is under-researched but the object is health service delivery.
This discusses telehealth and First Nations healthcare needs rather than the research system.
Health services paper on telehealth for First Nations care needs; healthcare delivery, not research.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 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".