Telehealth for rural kids:Post-disaster recovery
Bibliographic record
Abstract
Background: In response to the 2019/2020 Black Summer bushfires in regions of New South Wales (NSW), UNICEF Australia and Royal Far West (RFW) partnered to design and deliver a program to support the wellbeing and resilience of young children and to reduce the likelihood of long-term adverse effects. Experiencing a disaster of this nature can have an ongoing impact on a child. Regional, rural, and remote areas often face limited access to professional support and resources, emphasising the importance of innovative approaches such as telecare. Aim: To evaluate the effectiveness of a telecare program designed and implemented to support the recovery of children impacted by the Black Summer bushfires. Methods: A total of 135 children in regional, rural, and remote areas of NSW participated in individual occupational therapy, psychology and/or speech pathology telecare sessions. Data determining the effectiveness of the telecare program was collected using several measures including: satisfaction survey, Canadian Occupational Performance Measure (COPM), Goal Attainment Scale (GAS), and Strengths and Difficulties Questionnaire (SDQ). Results: Most children who attended telecare sessions indicated they felt listened to, enjoyed the sessions and learned new ways to feel better. COPM outcomes indicated that the children demonstrated a statistically significant improvement in their self-perceived performance and satisfaction of their identified goals. Overall, 86% of children attained or exceeded the pre-telecare goals they set using the GAS by the completion of their telecare sessions. There was a statistically significant improvement in children’s mental health outcomes post-telecare as measured by the SDQ, with a small to medium effect size. Conclusion: Occupational therapy, psychology and/or speech pathology telecare can be an effective strategy to support children’s mental health, recovery and goal achievement following a disaster such as bushfire. It offers children in geographically remote areas access to professional supports not immediately available in their local communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".