High Conflict Intervention Programs in Alberta: A Review and Recommendations
Bibliographic record
Abstract
This project has reviewed five programs developed and administered by Alberta Justice that deal with complex family law cases. In addition to reviewing relevant literature from other jurisdictions, several data collection strategies were implemented to answer the research questions identified in Section 1.2.1. Specifically, data were collected using the following methodologies: telephone interviews with program managers; review of Family Justice Services files; survey of stakeholders including judges/justices, lawyers, psychologists, social workers, and others; and an analysis of FOCIS evaluation forms supplied by Alberta Justice. Overall Program Success The information collected for this project clearly indicates that Alberta’s programs for high conflict families are successful. Respondents to the Stakeholder’s Survey were very positive about all the programs: a substantial majority thought that the programs were effective in reducing conflict, saving court time and costs, and facilitating settlements in both the short- and long-term (see Chapter 5.0). The Family Justice Services file reviews indicated that, for each program, the number of court orders decreased in the period following the intervention. Moreover, this decrease continued up to 30 months post-intervention (see Chapter 4.0). Participants who attended FOCIS were overwhelmingly positive about the program (see Chapter 6.0). They felt that they were more knowledgeable about the topics covered following completion of the course, that the coverage of the topics was “just right,” and that the course leaders were clear, easy to understand, and knowledgeable. A clear affirmation of the success of the program is the finding that 96% of participants said that they would recommend FOCIS to others. Issues Arising from the Review and Associated Recommendations Family Justice Services Files In conducting the Family Justice Services file review, it was apparent that the completeness of the files varied substantially across programs and locations. This lack of information limited the amount of data that could be collected from the files. Fortunately, obtaining access to the CASES and JOIN systems ameliorated this problem somewhat. The lack of complete data may well affect service provision, as well as research.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".