Social enterprise as a strategy to advance patient-oriented health services innovation: learning from the Alberta Family Integrated Care model
Bibliographic record
Abstract
This community case study outlines the conceptualization, development, implementation, and commercialization of the Alberta Family Integrated Care (Alberta FICare) model, offering insights into a unique way of sustaining patient-oriented innovations through social enterprise. Our team developed the Alberta FICare model to include families as partners in care in neonatal intensive care units (NICUs). Research phases of our model showed improved outcomes for neonates (shorter hospital stays), their families (greater caregiving self-efficacy, reduced psychosocial distress), and the health system (cost avoidance). Despite co-development of the model with families, providers, and leaders, rigorous testing (cluster randomized controlled trial), and province-wide scale-up (now standard of care in all 14 Alberta NICUs) efforts to sustain the model stalled due to shifting health system priorities. To address this challenge, we incorporated a social enterprise (Liminality Innovations Inc.) to sustain the model of care and support broader dissemination of family integrated care practices in NICUs beyond Alberta. While this strategy fostered sustainment and growth of our model, it also raised challenges. Some of these challenges included tackling perceptions within the research and practice communities that commercialization undermines research integrity. We share our experiences to highlight the potential of ethical, mission-driven commercialization through social enterprise to support innovation in learning health systems through ongoing interest holder engagement, responsible stewardship, and improving learning health system outcomes as the central goal.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".