Going to the Source: Creating a Citizenship Outcome Measure by Community-Based Participatory Research Methods
Bibliographic record
Abstract
OBJECTIVE: This study used participatory methods and concept-mapping techniques to develop a greater understanding of the construct of citizenship and an instrument to assess the degree to which individuals, particularly those with psychiatric disorders, perceive themselves to be citizens in a multifaceted sense (that is, not in a simply legal sense). METHODS: Participants were persons with recent experience of receiving public mental health services, having criminal justice charges, having a serious general medical illness, or having more than one of these "life disruptions," along with persons who had not experienced any of these disruptions. Community-based participatory methods, including a co-researcher team of persons with experiences of mental illness and other life disruptions, were employed. Procedures included conducting focus groups with each life disruption (or no disruption) group to generate statements about the meaning of citizenship (N = 75 participants); reducing the generated statements to 100 items and holding concept-mapping sessions with participants from the five stakeholder groups (N = 66 participants) to categorize and rate each item in terms of importance and access; analyzing concept-mapping data to produce citizenship domains; and developing a pilot instrument of citizenship. RESULTS: Multidimensional scaling and hierarchical cluster analysis revealed seven primary domains of citizenship: personal responsibilities, government and infrastructure, caring for self and others, civil rights, legal rights, choices, and world stewardship. Forty-six items were identified for inclusion in the citizenship measure. CONCLUSIONS: Citizenship is a multidimensional construct encompassing the degree to which individuals with different life experiences perceive inclusion or involvement across a variety of activities and concepts.
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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 | 0.044 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".