Five Years of Youth Engagement with Kids Help Phone Canada (Part 1): Phone, Chat, Text, and Peer-to-Peer Service Usage Nationally, Provincially, and Over Time
Bibliographic record
Abstract
Background: Child and youth mental health problems represent a substantial burden of illness in Canada, with appropriate services only inconsistently available. Charitable organizations, such as Kids Help Phone (KHP), are, therefore, crucial to filling system gaps by offering 24/7 phone, chat, texting, and peer-to-peer services. Methods: We describe the services provided by KHP, the volume of use for each service, and compare usage across Canada's provinces and territories for a 5-year period from January 2018 to December 2022. Trends seen during the COVID-19 pandemic are highlighted. Results: More than 1.5 million total number of conversations were held across texting, chat, and phone services over 5 years. Considerable growth is demonstrated between 2018 and 2022, and many of the highest peaks in volume occurred in March or April of 2020, the onset of the COVID-19 pandemic. The highest proportional volumes were consistently from the northern territories. Discussion: KHP cannot provide specialized or repeat services, nor can it alone meet the scale of unmet youth mental health needs across the country. Nonetheless, KHP plays a pivotal role in the Canadian mental health system. Efforts to understand the role that KHP and other e-mental health services like it play within the national mental health landscape should be intensified to aid in understanding unmet needs, identify system gaps, and make needed enhancements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".