Telephone outreach by volunteer navigators: a theory-based evaluation of an intervention to improve access to appropriate primary care
Bibliographic record
Abstract
Abstract Background: Pilot interventions were implemented in five different regions within a Canada-Australia participatory research programme to improve access to primary healthcare for vulnerable populations. In Québec, Canada, a telephone outreach program used volunteer patient navigators to help unattached persons from deprived neighbourhoods attach successfully to a family doctor newly-assigned to them from a centralized waiting list. According to our theory-based program logic model we evaluated the extent to which the volunteer navigator outreach helped patients reach and engage with their newly-assigned primary care team, have a positive healthcare experience, develop an enduring doctor-patient relationship, and reduce forgone care and emergency room use. Method: For the mixed-method evaluation, indicators were developed for all domains in the logic model and measured in a telephone-administered survey at baseline and three months later to determine if there was a significant difference. Semi-structured with a subsample of 13 survey respondents explored the mechanisms and nuances of intended effects. Results: Five active volunteers provided the service to 108 persons, of whom 60 agreed to participate in the evaluation. All surveyed participants attended the first visit, where 90% attached successfully to the new doctor. Indicators of abilities to access healthcare increased statistically significantly as did ability to explain health needs to professionals. The telephone outreach predisposed patient to have a positive first visit and have trust in their new care team, establishing a basis for an enduring relationship. Access difficulties, foregone care and use of hospital emergency rooms decreased dramatically after patients attached to their new doctors. Conclusions: As per the logic model, telephone outreach by volunteer navigators significantly increased patients’ abilities to seek, reach and engage with care and helped them attach successfully to newly-assigned family doctors. This light-touch intervention led to achievement of the intended policy goals for the centralized waiting list to increase population access to appropriate primary care and reducing forgone care and use of emergency rooms.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".