Bibliographic record
Abstract
Black youths experience poor mental health especially due to anti-Black racism. Research related to Black youths have been conducted on Black youths with little or no participation or engagement rather than with Black youths. This paper presents information from a dialogue on decolonizing nursing research. I draw on interviews and conversation cafes with around 120 Black youths in Canada to identify strategies for decolonizing research with Black youths. First, I reflect on my relations with the Indigenous land in which the study was conducted as well as my positionality as a Black woman. In this paper, I discuss how community based participatory action research can integrate capacity building component, amplify youth's voices and capitalize on the agency of youths as fruitful actors. I also reflect on the opportunities and benefits of decolonizing nursing research.
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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 | 0.071 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.034 | 0.038 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".