Improving Access to Specialist Care for an Aging Population
Bibliographic record
Abstract
Objective: The objective of the study is to examine the Champlain. BASE TM (Building Access to Specialists through eConsultation) eConsult service’s impact on access to care for older persons. Methods: We conducted a cross-sectional analysis of all eConsult cases submitted between April 15, 2011, and July 31, 2015, in which the patient was above the age of 65 years. Study data consisted of utilization data collected automatically by the service and responses to surveys completed by primary care providers at the conclusion of all eConsult cases. Results: A total of 1,796 cases were submitted for older persons between April 15, 2011, and July 31, 2015, accounting for 21.3% of all cases submitted during the study period. Specialists responded to cases in a median of 0.8 days. In 94% of cases, providers rated eConsult as having great or excellent value for themselves and their patients. Sixty-eight percent of eConsults did not require a face-to-face visit; only 28% of all cases resulted in a referral. Discussion: As they suffer from higher than average rates of comorbid disease and mobility issues, older persons stand to benefit from shorter wait times and better access to care, which the eConsult service can provide.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".