The determinants of effective inter-organization information sharing in the health capital planning process
Bibliographic record
Abstract
This qualitative study examines the determinants of effective inter-organization information sharing in the Health Capital Planning process (the process), primarily in the final stage of the process which focuses on the review of final expenses and release of a holdback. Using thematic analysis and building off a scoping review that was conducted in preparation for this study, we provide a framework for effective information sharing during the process. We interviewed 17 leaders from the Government of Ontario and hospitals across the province. The results of the interviews indicate that the most essential determinants of effective inter-organization information sharing in the process: organizational characteristics; reducing complex bureaucracies; preserving human resources and expertise; clear and standardized information; reducing policy changes; networks; negotiation abilities; information technology; training; record retention; and early planning. This study confirmed the need for effective intra-organization and interpersonal information sharing to achieve successful inter-organization information sharing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".