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Record W4366235770 · doi:10.1177/15394492231161283

Occupational Therapists in Patient Navigation: A Scoping Review of the Literature

2023· review· en· W4366235770 on OpenAlexaffabout
Kristina M. Kokorelias, Hardeep Singh, Alexandra Thompson, Amy Nesbitt, Jessica E. Shiers-Hanley, Michelle Nelson, Sander L. Hitzig

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2023
Typereview
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsSinai Health SystemHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsOperationalizationConceptualizationOccupational therapyExcellencePsychologyMedicineMedical educationComputer sciencePolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

This review seeks to understand the literature on patient navigator programs (PNPs) that employ occupational therapists (OTs), including the role (conceptualization), functions (operationalization) of OTs who work as patient navigators (PNs) and the settings and populations they serve. This review also mapped the role of PNs to the 2021 Competencies for Occupational Therapists in Canada. Scoping review methodology by Arksey and O'Malley (2005) was employed. Data were analyzed thematically and numerically to identify frequent patterns. Ten articles were included. Within PNPs, OTs worked in hospitals and communities, but their role was rarely well-defined. Five competency domains (i.e., communication and collaboration, culture, equity and justice, excellence in practice, professional responsibility, and engagement with the profession) were evident in existing PNPs that included OTs. This review supports the increasing interest in OTs as PNs by demonstrating the alignment between the OT competencies and roles and functions of OTs working within PNPs.

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)0.008
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0160.020
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.495
GPT teacher head0.653
Teacher spread0.158 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2023
Admission routes2
Has abstractyes

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