“Parental alienation” cases: Experiences of Ontario legal and mental health professionals
Bibliographic record
Abstract
Abstract This paper reports on two related studies on the experiences of Ontario therapists, parenting evaluators, child protection service (CPS) staff, and lawyers for parents, children and CPS about parent–child contact problem cases involving claims of parental alienation. One qualitative study was based on interviews with 62 professionals (45 parents' lawyers and 17 therapists) involved in reported Ontario cases between 2010 and 2022 where the court made a finding of parental alienation. The second qualitative study was based on nine focus groups with 50 Ontario professionals (31 children's lawyers, four CPS lawyers, five CPS workers and 10 parenting evaluators) about their experiences with this type of high conflict separation case. The majority of professionals in both studies found that parents in these cases are often very challenging clients. The professionals shared their frustration that the family justice system is slow to respond and has few effective legal responses or clinical resources that can provide appropriate services for these cases. The studies highlight the complexity of these cases and the need for the family justice system to better provide coordinated legal and clinical responses for these families. There is a need for more evidence‐based collaborative interdisciplinary practice, research and professional education.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".