Facilitators and barriers of sociodemographic data collection in Canadian health care settings: a multisite case study evaluation
Bibliographic record
Abstract
BACKGROUND: Despite growing awareness of the importance of social determinants of health, research remains limited about the implementation of sociodemographic data collection in Canadian health care settings. Little is known about the salient contextual factors that enable or hinder collection and use of social information to improve quality of care in clinical settings. This study examines the perceptions and experiences of managers and care providers to better understand how to support organizational efforts to collect and use sociodemographic data to provide equity-oriented care. METHODS: Case studies of three diverse urban health care settings employed semi-structured individual and group interviews with managers and care providers respectively to explore their experiences with implementation. Data was analyzed separately and in context for each site as part of an individual case study. A thematic analysis of interview transcripts was performed with an inductive approach to coding of segments of the text. Constructs of the Consolidated Framework for Implementation Research (CFIR) were used as an analytical framework to structure the data to support cross case comparisons of facilitators and barriers to implementation across settings. RESULTS: Several perceived facilitators and barriers to implementation were identified that clustered around three CFIR domains: intervention, inner setting and characteristics of individuals. Macro level (outer setting) factors were relatively unexplored. Sites were motivated by their recognition of need for social information to improve quality of care. Organizational readiness for implementation was demonstrated by priorities that reflected concern for equity in care, leadership support and commitment to an inclusive process for stakeholder engagement. Barriers included perceived relevance of only a subset of sociodemographic questions to service delivery, staff capacity and comfort with data collection as well as adequate resources (funding and time). CONCLUSION: Although system level mandates were underexplored, they may accelerate adoption and implementation of sociodemographic data collection in the presence of organizational readiness. Standardized tools integrated into information systems and workflows would support adequately trained personnel. More research is needed to understand important factors in rural health settings and with clinical application to inform care delivery pathways.
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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 | 0.045 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".