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Record W2766834498 · doi:10.1136/bmjopen-2017-015947

Implementation and impact of an online tool used in primary care to improve access to financial benefits for patients: a study protocol

2017· article· en· W2766834498 on OpenAlexafffundabout
Anjana Aery, Anne Rucchetto, Alexander Singer, Gayle Halas, Gary Bloch, Ritika Goel, Danyaal Raza, Ross Upshur, Jackie Bellaire, Alan Katz, Andrew D. Pinto

Bibliographic record

VenueBMJ Open · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsManitoba HealthEast Wellington Family Health TeamSinai Health SystemUniversity of TorontoLunenfeld-Tanenbaum Research InstitutePublic Health OntarioRegent Park Community Health CentreSt. Michael's HospitalWellesley Institute
FundersCanadian Institutes of Health ResearchSt. Michael’s Hospital FoundationResearch Manitoba
KeywordsMedicineProtocol (science)Primary careHealth services researchPublic healthFamily medicineMedical educationFinanceAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: Addressing the social determinants of health has been identified as crucial to reducing health inequities. However, few evidence-based interventions exist. This study emerges from an ongoing collaboration between physicians, researchers and a financial literacy organisation. Our study will answer the following: Is an online tool that improves access to financial benefits feasible and acceptable? Can such a tool be integrated into clinical workflow? What are patient perspectives on the tool and what is the short-term impact on access to benefits? METHODS: An advisory group made up of patients living on low incomes and representatives from community agencies supports this study. We will recruit three primary care sites in Toronto, Ontario and three in Winnipeg, Manitoba that serve low-income communities. We will introduce clinicians to screening for poverty and how benefits can increase income. Health providers will be encouraged to use the tool with any patient seen. The health provider and patient will complete the online tool together, generating a tailored list of benefits and resources to assist with obtaining these benefits. A brief survey on this experience will be administered to patients after they complete the tool, as well as a request to contact them in 1 month. Those who agree to be contacted will be interviewed on whether the intervention improved access to financial benefits. We will also administer an online survey to providers and conduct focus groups at each site. ETHICS AND DISSEMINATION: Key ethical concerns include that patients may feel discomfort when being asked about their financial situation, may feel obliged to complete the tool and may have their expectations falsely raised about receiving benefits. Providers will be trained to address each of these concerns. We will share our findings with providers and policy-makers interested in addressing the social determinants of health within healthcare settings. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov: NCT02959866. Registered 7 November 2016. Retrospectively registered. Pre-results.

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)0.065
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.065
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.048
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0040.003
Science and technology studies0.0080.004
Scholarly communication0.0050.004
Open science0.0050.005
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0340.007

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 head0.429
GPT teacher head0.649
Teacher spread0.219 · 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 designNon-randomized trial
Domainnot available
GenreProtocol

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

Citations14
Published2017
Admission routes3
Has abstractyes

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