Organizational and Systems-level Strategies for Building Hospital Research Capacity: A Scoping Review
Bibliographic record
Abstract
A critical component of achieving an equitable learning health system (LHS) is the establishment of an integrated research infrastructure. Hospitals are a critical component of a functioning LHS, however, many hospitals in Canada lack the required capacity to participate in, generate or apply research findings. There is a need to increase capacity for research in Canadian hospitals, most of which are community hospitals, using targeted organizational and systems-level strategies. However, to our knowledge, there has not been a systematic synthesis of literature reporting strategies to increase research capacity in acute care. The aim of this scoping review is to summarize previously reported organizational and/or systems level strategies to increase capacity for hospital-based research. Our secondary aim is to understand if and how strategies to increase research capacity have been implemented.
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 | 0.147 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".