Bibliographic record
Abstract
Introduction: The DreamGlobal study incorporates mobile health innovations in technology with SMS text messaging and task shifting of blood pressure measurement. Methods: In this pragmatic RCT study participants with uncontrolled hypertension on or off of medication, received culturally competent text messages over a one year period. All participants lived on First Nations reserves in remote and rural parts of Canada. Initial blood pressure screening was with an automated oscillometric device. Community Health Workers and nurses were trained to measure blood pressure with an approved oscillometric device which was Bluetooth enabled. Once participants were registered online, they received their blood pressure results on their own mobile phone as a text message and guidelines based text messages. Their primary health care provider also received the results as a fax. They were randomized to receive health behaviour change messages alone or active messages including a recommendation to see their health care provider if their blood pressure was above target. The baseline blood pressure was the mean of all readings in the first two months and the final blood pressure, the mean of the last two months. The main outcomes were change in blood pressure between the two groups (active or passive text messages) and the proportion achieving control. Results: One hundred twenty-seven subjects had hypertension. Blood pressure in the first two months of measurement was 143/85 mmHg over all. The blood pressure in the final two months of the study will be reported as will the change in blood pressure in both the active and passive text message groups and the proportion achieving blood pressure control in each group. Health literacy regarding the guideline’s based text messages was measured to determine if there was a difference between scores for the passive messages (all participants) and the active messages (active group only). Discussion: Achieving guidelines based management of hypertension is more challenging in remote, resource poor environments. Treatment focused SMS messages may improve hypertension management and allow the provision of guidelines based care even in rural and remote areas with health care resource challenges.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".