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Record W7000547488

The FORGE AHEAD clinical readiness consultation tool: a validated tool to assess clinical readiness for chronic disease care mobilization in Canada’s First Nations

2017· article· en· W7000547488 on OpenAlexaboutno aff

Bibliographic record

VenueArrow@dit (Dublin Institute of Technology) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousIntervention (counseling)Consistency (knowledge bases)Chronic careIdentification (biology)Internal consistencyQuality (philosophy)Citizen journalismTest (biology)
DOInot available

Abstract

fetched live from OpenAlex

Background: Given the astounding rates of diabetes and related complications, and the barriers to providing\ncare present in Indigenous communities in Canada, intervention strategies that take into account contextual factors\nsuch as readiness to mobilize are needed to maximize improvements and increase the likelihood of success and\nsustainment. As part of the national FORGE AHEAD Program, we sought to develop, test and validate a clinical\nreadiness consultation tool aimed at assessing the readiness of clinical teams working on-reserve in First Nations\ncommunities to participate in quality improvement (QI) to enhance diabetes care in Canada.\nMethods: A literature review was conducted to identify existing readiness tools. The ABCD – SAT was adapted\nusing a consensus approach that emphasized a community-based participatory approach and prioritized the\nknowledge and wisdom held by community members. The tool was piloted with a group of 16 people from 7\nprovinces and 11 partnering communities to assess language use, clarity, relevance, format, and ease of completion\nusing examples. Internal reliability analysis and convergence validity were conducted with data from 53 clinical\nteam members from 11 First Nations communities (3–5 per community) who have participated in the FORGE\nAHEAD program.\nResults: The 27-page Clinical Readiness Consultation Tool (CRCT) consists of five main components, 21\nsub-components, and 74 items that are aligned with the Expanded Chronic Care Model. Five-point Likert\nscale feedback from the pilot ranged from 3.25 to 4.5. Length of the tool was reported as a drawback but\nrespondents noted that all the items were needed to provide a comprehensive picture of the healthcare\nsystem. Results for internal consistency showed that all sub-components except for two were within\nacceptable ranges (0.77–0.93). The Team Structure and Function sub-component scale had a moderately\nsignificant positive correlation with the validated Team Climate Inventory, r = 0.45, p < 0.05.\nConclusions: The testing and validation of the FORGE AHEAD CRCT demonstrated that the tool is acceptable,\nvalid and reliable. The CRCT has been successfully used to support the implementation of the FORGE AHEAD Program\nand the health services changes that partnering First Nations communities have designed and undertaken to improve\ndiabetes care.

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)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.878

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.436
GPT teacher head0.616
Teacher spread0.180 · 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 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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