Brain asymmetry, immunity, handedness
Bibliographic record
Abstract
BACKGROUND: Limited access to specialist care remains a major barrier to health care in Canada, affecting patients and primary care providers alike, in terms of both long wait times and inequitable availability. We developed an electronic consultation system, based on a secure web-based tool, as an alternative to face-to-face consultations, and ran a pilot study to evaluate its effectiveness and acceptability to practitioners. METHODS: In a pilot program conducted over 15 months starting in January 2010, the e-consultation system was tested with primary care providers and specialists in a large health region in Eastern Ontario, Canada. We collected utilization data from the electronic system itself (including quantitative data from satisfaction surveys) and qualitative information from focus groups and interviews with providers. RESULTS: Of 18 primary care providers in the pilot program, 13 participated in focus groups and 9 were interviewed; in addition, 10 of the 11 specialists in the program were interviewed. Results of our evaluation showed good uptake, high levels of satisfaction, improvement in the integration of referrals and consultations, and avoidance of unnecessary specialist visits. A total of 77 e-consultation requests were processed from 1 Jan. 2010 to 1 Apr. 2011. Less than 10% of the referrals required face-to-face follow-up. The most frequently noted benefits for patients (as perceived by providers) included improved access to specialist care and reduced wait times. Primary care providers valued the ability to assist with patient assessment and management by having access to a rapid response to clinical questions, clarifying the need for diagnostic tests or treatments, and confirming the need for a formal consultation. Specialists enjoyed the improved interaction with primary care providers, as well as having some control in the decision on which patients should be referred. INTERPRETATION: This low-cost referral system has potential for broader implementation, once payment models for physicians are adapted to cover e-consultation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".