The Determinants of Effective Information Sharing in the Health Capital Planning Process
Bibliographic record
Abstract
The objective of this paper is to examine the determinants of effective information sharing in the health capital planning process, particularly the Ontario-Canada process. We conducted a scoping review which included 40 studies, and we interviewed 17 sector experts from Ontario to outline the determinants of most critical information sharing in the Health Capital Planning process. Due to the nature of capital planning which requires the involvement of numerous partners, we focused our study on inter-organization information sharing. Our study provides an inter-organization information-sharing framework for health capital projects. Our framework demonstrates that inter-organization information sharing is only effective if organizations also show effective interpersonal and intra-organization information sharing. We concluded that in order for organizations to successfully collaborate on developing an infrastructure project, they must ensure an effective flow of information from within and between the organizations involved. We found that the determinants of effective inter-organization information sharing in the health capital planning process are: human resources and expertise; incentives and rewards; clear and standardized information; record retention; reducing complex bureaucracies; organizational characteristics; networks; negotiation abilities; alignment of goals; quality; and early planning. The findings of this paper can guide organizations and system planners to improve their inter-organization information sharing, which could promote stronger accountability and more efficient use of resources in the health capital planning process.
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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 | 0.048 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".