Ethnicity and mental health: Conceptualization, definition and operationalization of ethnicity from a Canadian context
Bibliographic record
Abstract
The current study provides a critical review of Canadian studies on ethnicity and mental health with respect to the definition, conceptualization and operationalization of ethnicity. It provides a discussion on the methodological issues related to these factors and their implications to guide future research and enable comparability of results across studies. Sociological Abstracts, PsycINFO, MEDLINE and CINAHL were used to identify relevant Canadian articles published between January 1980 and December 2004. The review highlights a number of key issues for future researchers to consider such as the need for: 1) clear rationales as to why ethnicity is important to their outcome of interest; 2) clarity on the definition of ethnicity, which affects its conceptualization and operationalization; 3) a theoretically driven conceptualization of ethnicity, which should be related to the research question of interest; and 4) clear rationales for the decisions made regarding the data source used, the operationalization of ethnicity, and the ethnic categories included in their studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".