Understanding social inclusion: A directed content analysis
Bibliographic record
Abstract
BACKGROUND: Social inclusion can be defined as a process that enhances opportunities for social participation, strengthens social bonds, and ensures equitable access to opportunities and decision-making. It emphasizes the interconnectedness of society and the active roles individuals play in upholding shared values and responsibilities. This study aimed to explore factors contributing to social inclusion from the perspective of families in Kingston, Ontario who self-identified as having a history of adversity and as being resilient during the COVID-19 pandemic. METHODS: Participants consisted of a maximum variation sample of families who demonstrated family level resilience during the COVID-19 pandemic. Focus groups and semi-structured in-depth interviews were conducted to allow participants to explore what helped or hindered their family resilience during and beyond the pandemic. Using directed content analysis, line-by-line coding of interview transcripts was conducted to explore the fit between data and an existing social inclusion framework. RESULTS: The majority of data fit meaningfully into the dimensions established in the social inclusion framework:(1) Quality Education; (2) Innovation & Technology; (3) Governmental Policies & Laws; (4) Transportation & Infrastructure; (5) Employment & Organizations; (6) Poverty & Economy; (7) Medical & Health; and (8) Community & Culture. However, we noted that some dimensions could be both sources of and barriers to social inclusion. Additionally, our study identified specific elements not discussed in the original inclusion framework, including informal education, public gathering spaces, nature, the social dimensions of poverty, and mental health care. CONCLUSIONS: Participants perceived social inclusion as a facilitator of resilience. Interventions targeting the many dimensions of social inclusion must be implemented to drive positive community transformation.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".