Five year cost savings of a multimodal treatment program for child sexual abuse (CSA): a social return on investment study
Bibliographic record
Abstract
BACKGROUND: Specialized mental health services for the treatment of Child Sexual Abuse (CSA) are generally expensive and labour intensive. They require a trauma-informed approach that may involve multiple services and therapeutic modalities, provided over the course of several months. That said, given the broad-ranging, long term negative sequelae of CSA, an evaluation of the cost-benefit analysis of treatment is clearly justified. METHODS: We performed a Social Return on Investment (SROI) analysis of data gathered as part of the treatment program at the Be Brave Ranch in Edmonton, Canada to determine the value-for-money of the services provided. We endeavoured to take a conservative, medium-term (5 year) perspective; this is in contrast to short term (1-2 year) effects, which may rapidly dissipate, or long term (15-20 year) effects, which are likely diffuse and difficult to measure. As such, our analysis was based on an average annual intake of 100 children/adolescents (60:40 split) and their families, followed over a five-year timeframe. Financial proxies were assigned to benefits not easily monetized, and six potential domains of cost savings were identified. RESULTS: Our analyses suggest that each dollar spent in treatment results in an average cost savings of $11.60 (sensitivity analysis suggests range of 9.20-12.80). The largest value-for-money was identified as the domain of crisis prevention, via the avoidance of rare but costly events associated with the long term impacts of CSA. Somewhat surprisingly, savings related to the area of criminal justice were minimal, compared to other social domains analysed. Implications are discussed. CONCLUSIONS: Our results support the cost effectiveness of the investment associated with specialized, evidence-based early interventions for CSA. These approaches alleviate severe, negative outcomes associated with CSA, resulting in both economic savings and social benefits. These findings rest upon a number of assumptions, and generalizability of these results is therefore limited to similar programs located in comparable areas. However, the SROI ratio achieved in this analysis, in excess of $11:1, supports the idea that, while costly, these services more than pay for themselves over time.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".