Changes in patterns and referrals of Nova Scotians’ seeking mental health care through 2-1-1 Referral Service
Bibliographic record
Abstract
Context 2-1-1 is a designated phone help line in Canada and the US that provides referrals for individuals to local government and community-based services for a wide variety of needs, including social, health care, and mental health services. 2-1-1 Nova Scotia has operated since 2013 as a nonprofit that offers 24/7 assistance. Use of 2-1-1 contact report data for mental health systems research has been limited, however it has the potential to reveal challenges associated with access to care and unmet community needs. While most health care data is generated based on people who access services, analysis of 2-1-1’s caller data provides an opportunity to analyse the types and quantity of needs that people are looking to have met. Objective Analyse the trends in mental health-related call volume and referrals over the study period, including changes that occurred during the Covid-19 pandemic. Analyse mental health calls by caller type (individual, people seeking help for someone else, service provider) and explore how any identified patterns vary by location in province, gender, and age. Study Design Secondary analysis of an administrative dataset Dataset 2-1-1 Nova Scotia Contact Report dataset Population Studied Individuals who contacted 2-1-1 Nova Scotia from January 2013 to June 2023 Intervention/Instrument N/A Outcome Measures Calls related to mental health Results An increase was seen in absolute number of calls related to mental health and proportion of total calls related to mental health, from 5,535 and 6% in the first six months of 2013 to 36,226 and 30% in the first six months of 2023. Males represent 48% of the mental health callers vs only 34% for non-mental health calls. Adults, 35-54 years, represent 59% of the mental health calls, however only 41% of the non-mental health calls. A total of 111 mental health calls resulted in unmet needs since March 2020, with health zone 1 (a rural portion of the province) having the highest proportion of mental health calls with no referral. Conclusions Data analysis depicts a substantial increase in Nova Scotians seeking mental health care, particularly beginning in February 2022. Adult males are the most likely demographic to seek mental health care through 2-1-1. Further analysis of the alignment of programs available and community needs, particularly for zones outside the major urban center, could improve access to mental health services for Nova Scotians.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".