Dreaming of a Solution: D.R.E.A.M.-O.F. a Mental Health Promotion Program for Children and Their Families on Mental Health Waitlists
Bibliographic record
Abstract
The mental health rights of Canadian children are severely under met. To date, social policies advocate for strategies to reduce mental health service wait times, but little action has been taken to meet the needs of children and their families on waitlists. D.R.E.A.M. is a program that focuses on educating children about mental health, reducing stigma attached to mental health illness, and teaching tools for resilience. D.R.E.A.M. does not, however, currently address families. The current study involved the development of an adapted version of the D.R.E.A.M. program; D.R.E.A.M.-O.F. Through the creation of five family-based units, grafted onto the existing D.R.E.A.M. program, D.R.E.A.M.-O.F. aimed at providing families on mental health waitlists with the skills to begin addressing concerns associated with resilience promotion. The developed units were grounded in a literature-based and stakeholder-engaged needs assessment. A comparison of pre- and post-measure results from this pilot study indicated an improvement in child mental health symptoms, an increase in family functioning, and promotion of daily meaning for both children and adults. Given the encouraging findings from the pilot study of the program, further research regarding the application of this program is warranted. Future directions may include a program delivery in a web-based format, with video instructions and downloadable activities, with the goal of enhancing wellbeing, prior to standard mental health care services. The online format should make it more easily accessible for families and was recommended by stakeholders. Ultimately, the goal is for children and families to develop mental health-enhancing skills and reduce service time needed, thus hopefully shortening therapy waitlists.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".