Online peer support interventions for chronic conditions: a scoping review protocol
Bibliographic record
Abstract
Introduction Peer support is receiving increasing attention as both an effective and cost-effective intervention method to support the self-management of chronic health conditions. Given that an increasing proportion of Canadians have internet access and the increasing implementation of web-based interventions, online peer support interventions are a promising option to address the burden of chronic diseases. Thus, the specific research question of this scoping review is the following: What is known from the existing literature about the key characteristics of online peer support interventions for adults with chronic conditions? Methods and analysis We will use the methodological frameworks used by Arksey and O’Malley as well as Levac and colleagues for the current scoping review. To be eligible for inclusion, studies must report on adults (≥18 years of age) with one of the Public Health Agency of Canada chronic conditions or HIV/AIDS. We will limit our review to peer support interventions delivered through online formats. All study designs will be included. Only studies published from 2012 onwards will be included to ensure relevance to the current healthcare context and feasibility. Furthermore, only English language studies will be included. Studies will be identified by searching a variety of databases. Two reviewers will independently screen the titles and abstracts identified by the literature search for inclusion (ie, level 1 screening), the full text articles (ie, level 2 screening) and then perform data abstraction. Abstracted data will include study characteristics, participant population, key characteristics of the intervention and outcomes collected. Dissemination This review will identify the key features of online peer support interventions and could assist in the future development of other online peer support programmes so that effective and sustainable programmes can be developed.
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 | 0.109 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.091 | 0.019 |
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".