T102. TELEPROM-Y: IMPROVING ACCESS AND EXPERIENCE OF MENTAL HEALTHCARE FOR YOUTH THROUGH VIRTUAL MODELS OF CARE
Bibliographic record
Abstract
Abstract Background About 1 in 5 youth have a mental illness, with 75 percent of all mental illnesses having their onset in childhood or adolescence (Kim-Cohen et al., 2003). In Ontario, 157,900 youth rated their mental health as fair or poor, a significant increase from 2007 (Boak et al., 2014). Not only do mental health concerns cause difficulties at onset, they can also disrupt important life transitions and developmental milestones, as well as being burdensome throughout the individual’s lifespan (Ratnasingham et al., 2012). Consequently, new care approaches are needed. The TELEPROM-Y project will evaluate outpatient health care delivery using InputHealth’s electronic Collaborative Health Record (CHR) at London Health Sciences Centre, St. Joseph’s Health Care London, Woodstock General Hospital, and community agencies including Youth Opportunities Unlimited, WAYS Mental Health Support, and Leads Employment Services. Methods 120 youth (ages 14–25) will be recruited from the caseloads of 46 mental healthcare providers. Participants will use a smartphone application (app) to connect to the Collaborative Health Record. Semi-structured interviews will be conducted at baseline, 6, and 12 months. This is a participatory action research project utilizing a pre-post, mixed-methods design. A standardized evaluation framework will be instituted to facilitate systematic effectiveness, economic, ethical, and policy analyses. Some of the functions of the app, available for Apple and Android phones, include: making/changing/cancelling appointments; text messaging; emailing, and filling out questionnaires/surveys. If the youth are unable to attend a scheduled appointment in person, the care-provider and youth can have a virtual visit, similar to FaceTime or Skype. Virtual visits should reduce missed appointments. Results Descriptive information thus far of 104 participants: Psychotic Disorder (e.g. schizophrenia) (13, 12.6%), Developmental handicap (e.g. Autism) (7, 6.8%), Anxiety Disorder (e.g. PTSD) (73, 70.9%), Disorder of childhood/adolescence (e.g. ADHD) (37, 35.9%), Substance-related disorder (13, 12.6%), Personality Disorder (17, 16.5%), Mood Disorder (e.g. depression, bipolar mood disorder) (70, 68.0%), Unknown (4, 3.9%), Other (19, 18.4%). Discussion We anticipate that through the usage of the TELEPROM-Y app the participant and care-provider experience will be enhanced, leading to 1) improved healthcare outcomes and patient quality of life, and 2) reduced healthcare costs by preventing hospitalization and reducing the need for face-to-face outpatient visits.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".