Challenges and considerations in virtual (remote) parenting plan evaluations: Evaluator experiences and perceptions
Bibliographic record
Abstract
Abstract This study aimed to explore parenting plan evaluators' experiences (child custody evaluators) as they adopted virtual (remote) methods for conducting evaluations during the global pandemic. This study used a retrospective cross‐sectional design, surveying evaluators at two different points in time to assess their experiences with conducting virtual evaluations. Evaluators were recruited from the roster list of the Office of the Children's Lawyer in Ontario and were asked to complete an online survey at the start of the pandemic (April 2020) and then again 7 months later (November 2020). One hundred sixty‐one ( n = 161) participants completed the online survey at time 1, and sixty‐one ( n = 61) at time 2. Most of the participants had received fewer than 5 hours of professional training related to the use of technology before the global pandemic. Findings indicate that while some evaluators adapted to virtual methods, concerns about confidentiality, third‐party influence, and rapport building persisted. Several factors impacted the increased confidence in using the technology, including training, supervision, the support provided to the evaluators, and the culture of the family court system that embraced the use of technology during the pandemic. Implications include when evaluators should consider virtual methods, employ a hybrid approach, and when virtual methods may be inappropriate.
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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 | 0.036 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".