Siloed mentality, health system suboptimization and the healthcare symphony: a Canadian perspective
Bibliographic record
Abstract
Measuring and optimizing a health system is challenging when patient care is split between many independent organizations. For example, patients receive care from their primary care provider, outpatient specialist clinics, hospitals, private providers and, in some instances, family members. These silos are maintained through different funding sources (or lack of funding) which incentivize siloed service delivery. A shift towards prioritizing patient outcomes and keeping the patient at the centre of care is emerging. However, competing philosophies on patient needs, how health is defined and how health is produced and funded is creating and engraining silos in the delivery of health services. Healthcare and health outcomes are produced through a series of activities conducted by diverse teams of health professionals working in concert. Health professionals are continually learning from each patient interaction; however, silos are barriers to information exchange, collaborative evidence generation and health system improvement. This paper presents a systems view of healthcare and provides a systems lens to approach current challenges in health systems. The first part of the paper provides a background on the current state and challenges to healthcare in Canada. The second part presents potential reasons for continued health system underperformance. The paper concludes with a system perspective for addressing these challenges.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.031 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 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".