Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The presence of lotteries can be witnessed worldwide. Charitable lotteries are often portrayed as 'good works', and recently, hospitals have utilized them as a popular fundraising vehicle to raise necessary funds to help achieve organizational goals and objectives. Research indicates that lotteries contribute to gambling-related harms; however, research into charitable lotteries has been underdeveloped. Both the gambling and the health care industries are complex and evolving, consisting of many interacting stakeholders with often different and competing interests. This article seeks to present systems thinking as a conceptual framework to help fill the gap in understanding the use of gambling within hospitals and its possible benefits and unforeseen negative consequences. Addressing the gap in knowledge is important to help inform decision making aimed at reducing gambling-related harms. METHOD: This article proposes how the school of systems thinking, specifically framing hospitals as complex adaptive systems and system dynamics modelling, can be utilized to understand the policy implications of the adoption of lotteries as a revenue source for hospitals. CONCLUSION: Hospitals have a duty to care, inform and protect. Hospital charitable lotteries have become big business; however, its incorporation into critical funding strategies needs to be carefully understood. Systems thinking theory and methodologies provide an integrated approach to examine this dynamic and evolving fundraising initiative. Findings from this article can inform the development of action strategies, including policy development at multiple levels.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".