The social cost of gambling: A systematic review of impacts and a targeted review of costing studies
Bibliographic record
Abstract
Background: Gambling related harm is well established and documented. However, there is far less information regarding the appropriate way to conceptualise and quantify the costs of gambling in economic terms. This prompted a review of (a) the current state of knowledge regarding what aspects should feature in any accounting of gambling-related impact; and (b) specific projects that have attempted to implement such a costing. Methods: A systematic review of the literature included a search of 11 electronic databases for publications on gambling-related harm and quantification of gambling costs published between 2010 and February 2016 (inclusive) written in English. A targeted review of the grey literature used websites and an unlimited time frame to identify attempts to quantify gambling costs. Both the systematic and targeted reviews involved a narrative synthesis of the publications. Results: Twenty-five of the 173 peer-reviewed publications found in the systematic literature search met the inclusion criteria for measuring the impact of gambling related harms and were reviewed by impact level: individual, affected others, and community. Three Australian and 1 Canadian report found in the targeted literature review quantified the costs of gambling. Conclusions: This study revealed there is a need for a standardised comprehensive methodology for identifying and measuring the costs of gambling. In particular, attention should be focused on how best to quantify and measure the extent and experience of gambling related harm in order to provide an accurate economic estimate. This will allow for comparisons between populations and inform policy on minimising gambling related-harm.
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 | 0.023 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".