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Record W2972257789 · doi:10.1186/s12954-019-0322-6

Supporting peer researchers: recommendations from our lived experience/expertise in community-based research in Canada

2019· article· en· W2972257789 on OpenAlexaffabout
Francisco Ibáñez-Carrasco, James R. Watson, James E. Tavares

Bibliographic record

VenueHarm Reduction Journal · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsHealth psychologyInclusion (mineral)HarmPsychologyPublic relationsPeer supportHuman immunodeficiency virus (HIV)Action researchIntervention (counseling)Action (physics)Medical educationPublic healthNursingSocial psychologyMedicinePolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Community-based research in HIV in Canada is a complex undertaking. Including peer researchers living with HIV meaningfully is intricate and costly. However, this inclusion guarantees results that translate to community action, policy-making, and public awareness. Including HIV+ peer researchers expedites the path from research to intervention. However, we must constantly review our support in light of three implicit tasks performed by peer researchers: constant disclosure, emotional labor, and advocating for meaningful participation. Our team offers four pillars of support to reduce harm and strengthen the self-determination, confidence, advocacy, and impact for HIV+ peer researchers. The provision of emotional, instrumental, educational, and cultural/spiritual support might seldom be standardized within a study, but to successfully engage in community-based research, study teams must articulate what support can be offered in each area.

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)0.109
metaresearch head score (Gemma)0.199
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.919
Threshold uncertainty score0.586

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1090.199
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.005
Science and technology studies0.0380.020
Scholarly communication0.0270.015
Open science0.0110.024
Research integrity0.0110.018
Insufficient payload (model declined to judge)0.0100.002

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 head0.732
GPT teacher head0.590
Teacher spread0.141 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations54
Published2019
Admission routes2
Has abstractyes

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