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Record W6926533540 · doi:10.25384/sage.c.6007509

Implementing patient navigator programmes within a hospital setting in Toronto, Canada: A qualitative interview study

2022· other· en· W6926533540 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2022
Typeother
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorQualitative researchImplementation researchHealth careAcute careWork (physics)Patient carePatient safetyPatient experience

Abstract

fetched live from OpenAlex

Objectives:This study sought to identify the organisation and system level barriers and facilitators influencing the implementation of patient navigator programmes in one acute care hospital system in Toronto, Canada.Methods:A qualitative descriptive approach informed by the Consolidated Framework for Implementation Research. Data were collected using in-depth interviews and analysed thematically.Results:Thirty-eight individuals participated in interviews (17 community, 21 acute care hospital), including 24 frontline clinicians and 14 programme directors, health care leaders and managers. Implementation of patient navigator programmes was dependent on: (1) a clear consensus on the unique need for patient navigators; (2) champions to promote patient navigation; (3) programme ownership and accountability; (4) external system and organisational landscape and (5) implementation climate. Appropriate mechanisms of communication were found to have impacted each factor as a barrier or facilitator to programme implementation.Conclusion:Strategies for implementing patient navigator programmes into hospital clinical practice should include incorporating evidence to support the programme, considering mechanisms to enable collaborative communication, and the integration of frameworks to facilitate programme integration into the current practices within the organisation.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0310.000

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 head0.040
GPT teacher head0.367
Teacher spread0.327 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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