Comparing impact of a holistic patient centered navigation model to an online navigation service on health care utilization
Bibliographic record
Abstract
Context Social prescribing has been linked to better access, improved health and wellbeing, and decreased health care utilization (HCU). However, very few randomized controlled trials have been conducted and have not assessed HCU. Objective We aim to compare the effects of two social prescribing (SP) lay navigation models on HCU in a randomized controlled trial. Population Primary care providers in 12 Ottawa and Sudbury (Ontario, Canada) practices referred their patients with health or social needs to access needed SP services. Study design/intervention These Patients were randomized to either receive the Access to Resources in the Community (ARC) SP navigation service; a holistic, patient-centered navigation services, or the provincially funded Ontario-211 online/telephone information and remote navigation SP services. Dataset Of the 326 enrolled patients, 150 consented and had their data successfully linked to health administrative data housed at ICES (ARC=83, Ontario-211: 67). Outcome Measures We compared the pre (Year -1/-2) - post (Year 0 and Year +1) differences in the number of outpatient and primary care visits in the ARC and 211 arms using linear regressions. We compared the odds ratio of patients having >1 emergency department (ER) visit and >1 hospitalization in the post-intervention years (Year 0 and Year +1) in the ARC and 211 arms using logistic regressions, while adjusting for pre-intervention HCU. Regression models were all adjusted with sociodemographic covariates. Results The adjusted 211-ARC difference (95% confidence interval (CI)) in outpatient visits was 1.0 (-1.6, 3.5) in Year 0 and 2.4 (-0.3, 5.1) in Year +1, and in primary visits was 0.7 (-0.6, 1.9) in Year 0 and 1.0 (-0.6, 2.6) Year +1; both in favour of the ARC. The odds ratio (95 CI) of ARC relative to 211 for ER visits was 0.7 (0.3, 1.5) in Year 0 and 1.7 (0.7, 4.1) in Year +1, and for hospitalizations was 1.2 (0.4, 3.3) in Year 0 and 1.8 (0.5, 6.8) in Year +1 in favour of ARC. Conclusions There was a trend for reduced HCU for patients in the ARC arm, although these results were not statistically significant. This study suggests that the ARC holistic patient navigation approach may be beneficial in reducing HCU and warrants further investigation with larger sample sizes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".