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Record W3011852104 · doi:10.1177/1355819620911679

Exploring the role of lay and professional patient navigators in Canada

2020· article· en· W3011852104 on OpenAlexaffabout
Amy Reid, Shelley Doucet, Alison Luke

Bibliographic record

VenueJournal of Health Services Research & Policy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsHealth Sciences CentreUniversity of New Brunswick
Fundersnot available
KeywordsThematic analysisContext (archaeology)Qualitative researchNursingWork (physics)PsychologyMedicineMedical educationSociologyGeography

Abstract

fetched live from OpenAlex

OBJECTIVES: To explore the roles of patient navigators in different settings and situations for various patient populations and to understand the rationale for implementing lay and professional models of patient navigation in a Canadian context. METHODS: A qualitative descriptive design was applied, using interviews with 10 patient navigators from eight Canadian provinces, and Braun and Clarke's six phases of thematic analysis to guide the analysis of interview transcripts. RESULTS: Findings indicate that a patient navigator's personality and experience (personal and work-related) may be more important than their specific designation (i.e. lay or professional). CONCLUSIONS: Lay and professional navigators in Canada appear to be well suited to provide navigational services across populations. This study has the potential to inform future research, policy, and the delivery of navigation programmes in Canada.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.560

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0240.009
Scholarly communication0.0050.002
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.184
GPT teacher head0.505
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations44
Published2020
Admission routes2
Has abstractyes

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