Transforming Households with Refraction and Innovative Financial Technology (THRIFT): study protocol for a randomised controlled trial of vision interventions and online banking among the elderly in Kurigram
Bibliographic record
Abstract
INTRODUCTION: Presbyopia, difficulty in seeing close-ups, affects a billion people globally. Mobile financial services (MFS) have been mandated since January 2021 for Bangladesh government social safety net payments, including old age allowance (OAA) and widow allowance (WA). We report the protocol for the Transforming Households with Refraction and Innovative Financial Technology randomised trial assessing the impact on the use of online banking of providing presbyopic safety net beneficiaries with reading glasses, and brief smartphone and mobile banking app training. METHODS AND ANALYSES: Eligible participants (n=484) are OAA (men aged 65-70 years; women aged 62-70) or WA recipients (women aged 48-60) with presbyopia as their only vision problem, passing a smartphone-based test of numeracy, cognition and dexterity, and not currently owning a smartphone or independently using MFS. All participants receive smartphones loaded with a mobile banking app and a transaction-tracking app and are randomised 1:1 to receive immediate free near-vision glasses and half-day training for smartphone and banking app use (intervention), or glasses and training 12 months later (control). The primary outcome is the mean quarterly number of mobile bank transactions over the 12-month follow-up period, comparing study groups, with and without adjustment. Secondary outcomes include food security, healthcare access and social connectedness. ETHICS AND DISSEMINATION: The protocol was approved by ethics committees at Queen's University Belfast (reference #MHLS22_69) and BRAC James P Grant School of Public Health (reference #IRB-21 August'22-028). The trial is conducted in accordance with the Declaration of Helsinki and national regulations in Bangladesh, and results will be published in open-access, peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT05510687; ClinicalTrials.gov.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.085 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".