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Record W2306041431 · doi:10.1353/cpr.2016.0008

Community Involvement in Development of Evidence-Informed Recommendations for Rehabilitation for Older Adults Living With HIV

2016· article· en· W2306041431 on OpenAlexafffund
Patricia Solomon, Kelly K. O’Brien, Larry Baxter, Duncan MacLachlan, Greg Robinson

Bibliographic record

VenueProgress in community health partnerships · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsAIDS Committee of TorontoMcMaster University
FundersCanadian Institutes of Health Research
KeywordsRehabilitationHuman immunodeficiency virus (HIV)GerontologyCommunity-based participatory researchMedicineMEDLINEClinical PracticePsychologyFamily medicinePhysical therapyParticipatory action researchPolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Involving community in development of clinical practice guidelines (CPGs) can decrease the gap between patient preferences and research evidence. OBJECTIVE: To incorporate meaningful participation of people living with human immunodeficiency virus (HIV; people living with HIV [PHAs]) in the development of evidence informed recommendations for rehabilitation practice. METHODS: PHAs were involved in a process to develop practice recommendations internally as members of a project team and externally through formal endorsement of the recommendations. LESSONS LEARNED: Lessons learned include 1) providing time to develop as a team and understand the roles, biases, and expertise of each member, 2) engaging community in initial discussions to determine the most meaningful involvement, 3) realizing that participation in research may trigger anxiety and stress in community members, 4) developing terms of reference to clarify roles and expectations, 5) providing opportunities for skill development, and 6) conducting formal evaluation of the process and satisfaction of community. CONCLUSION: Meaningful inclusion of community can improve the quality of practice guidelines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.557
GPT teacher head0.524
Teacher spread0.033 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
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

Citations10
Published2016
Admission routes2
Has abstractyes

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