Electronic consultation in correctional facilities worldwide: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the use of and evidence for eConsult in correctional facilities worldwide. DESIGN: Scoping review. DATA SOURCES: Three academic databases (MEDLINE, Embase and CINAHL) were searched to identify papers published between 1990 and 2020 that presented data on eConsult use in correctional facilities. The grey literature was also searched for any resources that discussed eConsult use in correctional facilities. Articles and resources were excluded if they discussed synchronous, patient-to-provider or unsecure communication. The reference lists of included articles were also hand searched. RESULTS: Of the 226 records retrieved from the academic literature search and 595 from the grey literature search, 22 were included in the review. Most study populations included adult male offenders in a variety of correctional environments. These resources identified 13 unique eConsult services in six countries. Six of these services involved multiple medical specialties, while the remaining services were single specialty. The available evidence was organised into five identified themes: feasibility, cost-effectiveness, access to care, provider satisfaction and clinical impact. CONCLUSIONS: This study identified evidence that the use of eConsult in correctional facilities is beneficial and avoids unnecessary transportation of offenders outside of the facilities. It is feasible, cost-effective, increases access to care, has an impact on clinical care and has high provider satisfaction. Some gaps in the literature remain, and we suggest further research on patient satisfaction, enablers and barriers to implementation, and women, youth and transgender populations in this setting to inform service providers and stakeholders. Despite some gaps, eConsult is evidently an important tool to provide timely, high-quality care to offenders.
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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 | 0.020 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".