Introduction to the Coproduction of Supervision Standards for Digital Peer Support: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Digital peer support enhances engagement in mental and physical health services despite barriers such as location, transportation, and other accessibility constraints. Digital peer support involves live or automated peer support services delivered through technology media such as peer-to-peer networks, smartphone apps, and asynchronous and synchronous technologies. Supervision standards for digital peer support can determine important administrative, educative, and supportive guidelines for supervisors to maintain the practice of competent digital peer support, develop knowledgeable and skilled digital peer support specialists, clarify the role and responsibility of digital peer support specialists, and support specialists in both an emotional and developmental capacity. OBJECTIVE: Although digital peer support has expanded recently, there are no formal digital supervision standards. The aim of this study is to inform the development of supervision standards for digital peer support and introduce guidelines that supervisors can use to support, guide, and develop competencies in digital peer support specialists. METHODS: Peer support specialists that currently offer digital peer support services were recruited via an international email listserv of 1500 peer support specialists. Four 1-hour focus groups, with a total of 59 participants, took place in October 2020. Researchers used Rapid and Rigorous Qualitative Data Analysis methods. Researchers presented data transcripts to focus group participants for feedback and to determine if the researcher's interpretation of the data match their intended meanings. RESULTS: We identified 51 codes and 11 themes related to the development of supervision standards for digital peer support. Themes included (1) education on technology competency (43/197, 21.8%), (2) education on privacy, security, and confidentiality in digital devices and platforms (33/197, 16.8%), (3) education on peer support competencies and how they relate to digital peer support (25/197, 12.7%), (4) administrative guidelines (21/197, 10.7%), (5) education on the digital delivery of peer support (18/197, 9.1%), (6) education on technology access (17/197, 8.6%), (7) supervisor support of work-life balance (17/197, 8.6%), (8) emotional support (9/197, 4.6%), (9) administrative documentation (6/197, 3%), (10) education on suicide and crisis intervention (5/197, 2.5%), and (11) feedback (3/197, 1.5%). CONCLUSIONS: Currently, supervision standards from the Substance Abuse and Mental Health Services Administration (SAMHSA) for in-person peer support include administrative, educative, and supportive functions. However, digital peer support has necessitated supervision standard subthemes such as education on technology and privacy, support of work-life balance, and emotional support. Lack of digital supervision standards may lead to a breach in ethics and confidentiality, workforce stress, loss of productivity, loss of boundaries, and ineffectively serving users who participate in digital peer support services. Digital peer support specialists require specific knowledge and skills to communicate with service users and deliver peer support effectively, while supervisors require new knowledge and skills to effectively develop, support, and manage the digital peer support role.
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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 | 0.048 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".