Use of the CONSIDER statement by eye health researchers when conducting and reporting research involving Indigenous peoples: an online survey
Bibliographic record
Abstract
BACKGROUND: Indigenous peoples experience worse eye health compared to non-Indigenous peoples. Service providers and researchers must avoid perpetuating this inequity. To help achieve this, researchers can use the CONSolIDated critERia for strengthening the reporting of health research involving Indigenous peoples (CONSIDER) statement. This study aimed to identify the degree to which the CONSIDER statement has been used by eye health researchers when conducting and reporting research with an Indigenous component, and how they perceive its relevance in their future research. METHODS: We used purposive sampling to recruit eye health researchers from any country who have undertaken research with an Indigenous component. The online survey collected quantitative and qualitative data and was analysed using descriptive statistics and reflexive thematic analysis. Responses were gathered on a four-point Likert scale (1 to 4), with four being the most positive statement. RESULTS: Thirty-nine eye health researchers from nine countries completed the survey (Aotearoa New Zealand, Argentina, Australia, Brazil, Canada, Colombia, Guatemala, Panama, Peru); almost two-thirds (n = 24) undertake epidemiological research. On average, participants disclosed only 'sometimes' previously reporting CONSIDER items (2.26 ± 1.14), but they thought the items were relevant to eye health research and were motivated to use these guidelines in their future research. Some participants requested clarity about how CONSIDER aligned with existing guidelines, and when and how to apply the statement. Others shared rich experiences of the benefits to their research of Indigenous leadership and collaboration. CONCLUSIONS: The CONSIDER statement is perceived as a valuable tool by these eye health researchers, and there are opportunities to maximise uptake and use, including increasing awareness of the statement, clarity about when it applies, and availability of institutional-level support.
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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 | 0.056 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".