The OnSPARK Data Platform: A Sector-Governed Infrastructure for Workforce Data for Planning and Quality Improvement in Long-Term Care
Bibliographic record
Abstract
The Long-Term Care (LTC) sector in Canada faces persistent challenges in staffing, including limited data to support workforce planning, quality improvement, and policy evaluation. The OnSPARK Data Platform was established in 2023 as a sector-governed, province-wide infrastructure to address these challenges. OnSPARK aggregates de-identified electronic health records from over 200 LTC homes in Ontario, representing approximately one-third of the sector. Many homes also submit shift-level payroll and scheduling-based staffing data to be linked to facility unit-level quality metrics. Near real-time, unit-level insights are provided through an interactive portal, while aggregated data support embedded research, performance benchmarking, and policy simulation. This article introduces the structure and functionality of the OnSPARK platform, describes its unique approach to staffing data collection and use, and outlines its potential to generate operational, clinical, and policy-relevant insights. By enabling ongoing access to workforce and care data, OnSPARK supports a learning health system model that strengthens decision-making.
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".