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Record W4247932186 · doi:10.2196/preprints.25528

Initial Training for Mental Health Peer Support Workers: Systematized Review and International Delphi Consultation (Preprint)

2020· preprint· en· W4247932186 on OpenAlexaboutno aff
Ashleigh Charles, Rebecca Nixdorf, Nashwa Ibrahim, Lion Gai Meir, Richard Mpango, Fileuka Ngakongwa, Hannah Nudds, Soumitra Pathare, Grace Ryan, Julie Repper, Heather Wharrad, Philip A. Wolf, Mike Slade, Candelaria Mahlke

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthDelphi methodPeer reviewThe InternetWork (physics)DelphiPeer supportMedical educationTraining (meteorology)PsychologyMedicinePolitical scienceNursingWorld Wide WebComputer scienceEngineeringGeographyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND Initial training is essential for the mental health peer support worker (PSW) role. Training needs to incorporate recent advances in digital peer support and the increase of peer support work roles internationally. There is a lack of evidence on training topics that are important for initial peer support work training and on which training topics can be provided on the internet. OBJECTIVE The objective of this study is to establish consensus levels about the content of initial training for mental health PSWs and the extent to which each identified topic can be delivered over the internet. METHODS A systematized review was conducted to identify a preliminary list of training topics from existing training manuals. Three rounds of Delphi consultation were then conducted to establish the importance and web-based deliverability of each topic. In round 1, participants were asked to rate the training topics for importance, and the topic list was refined. In rounds 2 and 3, participants were asked to rate each topic for importance and the extent to which they could be delivered over the internet. RESULTS The systematized review identified 32 training manuals from 14 countries: Argentina, Australia, Brazil, Canada, Chile, Germany, Ireland, the Netherlands, Norway, Scotland, Sweden, Uganda, the United Kingdom, and the United States. These were synthesized to develop a preliminary list of 18 topics. The Delphi consultation involved 110 participants (49 PSWs, 36 managers, and 25 researchers) from 21 countries (14 high-income, 5 middle-income, and 2 low-income countries). After the Delphi consultation (round 1: n=110; round 2: n=89; and round 3: n=82), 20 training topics (18 universal and 2 context-specific) were identified. There was a strong consensus about the importance of five topics: lived experience as an asset, ethics, PSW well-being, and PSW role focus on recovery and communication, with a moderate consensus for all other topics apart from the knowledge of mental health. There was no clear pattern of differences among PSW, manager, and researcher ratings of importance or between responses from participants in countries with different resource levels. All training topics were identified with a strong consensus as being deliverable through blended web-based and face-to-face training (rating 1) or fully deliverable on the internet with moderation (rating 2), with none identified as only deliverable through face-to-face teaching (rating 0) or deliverable fully on the web as a stand-alone course without moderation (rating 3). CONCLUSIONS The 20 training topics identified can be recommended for inclusion in the curriculum of initial training programs for PSWs. Further research on web-based delivery of initial training is needed to understand the role of web-based moderation and whether web-based training better prepares recipients to deliver web-based peer support. CLINICALTRIAL

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 imitation

Not 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.

metaresearch head score (Codex)0.313
metaresearch head score (Gemma)0.329
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.687
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3130.329
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0110.008
Science and technology studies0.0040.006
Scholarly communication0.0050.005
Open science0.0030.008
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.546
GPT teacher head0.543
Teacher spread0.003 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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