Addressing the prevention and treatment of child sexual abuse in culturally and linguistically diverse (CALD) communities in Australia - PROJECT METHODOLOGY
Bibliographic record
Abstract
In 2016, a systematic literature review on child sexual abuse and ethnic minority communities was conducted to help address a long-standing gap in knowledge. Ethnic minorities refer to migrants in Western English-dominant multicultural countries (such as Australia, the US, UK, Canada, and New Zealand) who are non-mainstream in race, culture, language, and/or religion – the four main dimensions of ethnicity (O’Hagan, 1999). Four broad themes were explored – community awareness of child sexual abuse, culturally appropriate prevention, barriers to disclosure of child sexual abuse among ethnic minorities, and culturally appropriate treatment. Findings in relation to these four themes are only briefly described here. The full results are available from the Project Website. Based on all these findings from the systematic literature review, a multi-year project was designed, and Griffith University (GU) has contributed funding to it. The study is comprised of three intended stages, each developing and evaluating an education program to help build capacity across various community sectors. The first program is for the service sector, and aims to build awareness and knowledge of all the key issues that emerged from the review as well as clinical confidence and competence. The second program is for CALD parents/guardians, and aims to build awareness and knowledge about child sexual abuse and the role of governments regarding child safety in culturally sensitive ways as well as confidence and competence to protect their children. The third program is for the school sector, and aims to build awareness and knowledge about the pitfalls of only using universal or only using culturally tailored prevention programs as well as staff confidence and competence to respond supportively and culturally appropriately to disclosures.
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 | 0.059 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".