Youth Mental Health Services Utilization Rates After a Large-Scale Social Media Campaign: Population-Based Interrupted Time-Series Analysis
Bibliographic record
Abstract
BACKGROUND: Despite the uptake of mass media campaigns, their overall impact remains unclear. Since 2011, a Canadian telecommunications company has operated an annual, large-scale mental health advocacy campaign (Bell Let's Talk) focused on mental health awareness and stigma reduction. In February 2012, the campaign began to explicitly leverage the social media platform Twitter and incented participation from the public by promising donations of Can $0.05 for each interaction with a campaign-specific username (@Bell_LetsTalk). OBJECTIVE: The intent of the study was to examine the impact of this 2012 campaign on youth outpatient mental health services in the province of Ontario, Canada. METHODS: Monthly outpatient mental health visits (primary health care and psychiatric services) were obtained for the Ontario youth aged 10 to 24 years (approximately 5.66 million visits) from January 1, 2006 to December 31, 2015. Interrupted time series, autoregressive integrated moving average modeling was implemented to evaluate the impact of the campaign on rates of monthly outpatient mental health visits. A lagged intervention date of April 1, 2012 was selected to account for the delay required for a patient to schedule and attend a mental health-related physician visit. RESULTS: The inclusion of Twitter into the 2012 Bell Let's Talk campaign was temporally associated with an increase in outpatient mental health utilization for both males and females. Within primary health care environments, female adolescents aged 10 to 17 years experienced a monthly increase in the mental health visit rate from 10.2/1000 in April 2006 to 14.1/1000 in April 2015 (slope change of 0.094 following campaign, P<.001), whereas males of the same age cohort experienced a monthly increase from 9.7/1000 to 9.8/1000 (slope change of 0.052 following campaign, P<.001). Outpatient psychiatric services visit rates also increased for both male and female adolescents aged 10 to 17 years post campaign (slope change of 0.005, P=.02; slope change of 0.003, P=.005, respectively). For young adults aged 18 to 24 years, females who used primary health care experienced the most significant increases in mental health visit rates from 26.5/1000 in April 2006 to 29.2/1000 in April 2015 (slope change of 0.17 following campaign, P<.001). CONCLUSIONS: The 2012 Bell Let's Talk campaign was temporally associated with an increase in the rate of mental health visits among Ontarian youth. Furthermore, there appears to be an upward trend of youth mental health utilization in the province of Ontario, especially noticeable in females who accessed primary health care services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".