Canadians’ views on the use of routinely collected data in health research: a patient-oriented cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Little is known about Canadians' knowledge of and level of support for using administrative and other large, routinely collected data for health research, despite the benefits of this type of research to patients, health care systems and society. We sought to benchmark the views of Canadian adults on this topic. METHODS: Researchers and patient leaders of 3 joint and skin disease organizations codeveloped a cross-sectional online survey that was conducted between January and August 2017. The patient partners were engaged as full partners. Recruitment was mainly through the organizations' websites, email and social media. The survey captured respondents' initial perceptions, then (after background information on the topic was provided) elicited their views on the benefits of health research using routinely collected data, data access/privacy concerns, ongoing perceptions and educational needs. RESULTS: Of the 230 people who consented, 183 (79.6%) started the survey, and 151 (65.6%) completed the survey. Of the 151, 117 (77.5%) were women, 84 (55.6%) were British Columbians, 87 (57.6%) were university graduates, and 101 (66.9%) had a chronic disease. At the beginning of the survey, 119 respondents (78.8%) felt positively about the use of routinely collected data for health research. Respondents identified the ability to study long-term treatment effects and rare events (114 [75.5%]) and large numbers of people (110 [72.8%]) as key benefits. Deidentification of personal information was the top privacy measure (135 [89.4%]), and 101 respondents (66.9%) wanted to learn more about data stewards' granting access to data. On survey completion, more respondents (141 [93.4%]) felt positively about the use of routinely collected data, but only 87 (57.6%) were confident about data security and privacy. INTERPRETATION: Respondents generally supported the use of deidentified routinely collected data for health research. Although further investigation is needed with more representative samples, our findings suggest that additional education, especially about access and privacy controls, may enhance public support for research endeavours using these data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".