Using “Big Data” to Provide Insights into Early Adopters of Continuing Professional Development: An Example from Project ECHO
Bibliographic record
Abstract
INTRODUCTION: Mental health care is often managed in primary care with limited specialist support, particularly in rural and remote communities. Continuing professional development programs (CPD) can offer a potential solution to further mental health training; however, engaging primary care organizations (PCOs) can be challenging. The use of "big data" to identify factors influencing engagement in CPD programs has not been well studied. Therefore, the aim of this project was to use administrative health data from Ontario, Canada to identify characteristics of PCOs associated with early engagement in a virtual CPD program, Project Extension for Community Healthcare Outcomes (ECHO) Ontario Mental Health (ECHO ONMH) . METHODS: Ontario health administrative data for fiscal year 2014 was used to compare the characteristics of ECHO ONMH-adopting PCOs, and their patients, to nonadopter organizations (N = 280 vs. N = 273 physicians). RESULTS: ECHO-adopting PCOs did not differ with respect to physician age or years of practice, although PCOs with more female physicians were somewhat more likely to participate. ECHO ONMH adoption was more likely in regions with lower psychiatrist supply, among PCOs using partial salary payment models, and those with a greater interprofessional complement. Patients of ECHO-adopters did not differ on the basis of gender or health care utilization (physical or mental health); however, ECHO-adopting PCOs tended to have patients with less psychiatric comorbidity. DISCUSSION: Models such as Project ECHO, which deliver CPD to primary care, are advanced to address lack of access to specialist health care. These findings support the use of administrative health data to assess the implementation, spread, and impact of CPD.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".