Factors associated with mental health service use during the pandemic: Initiation and barriers
Bibliographic record
Abstract
Background: Scarce are the studies focusing on initiation of new mental health service use (MHSU) and distinguishing individuals who have sought services but have been unsuccessful in accessing these. Aims: Assessing the factors associated with initiating new MHSU as compared to no MHSU due to self-reported no need, no MHSU due to health system and personal barriers and MHSU using resources already in place. Methods: The sample included participants ( n = 16,435) in the five established regional cohorts of the Canadian Partnership for Tomorrow’s Health (CanPath) who responded to the CanPath COVID-19 health surveys (May–December 2020 and January–June 2021). Multinomial regression analyses were carried out to study MHSU since the pandemic (March 2020) as a function of predisposing, enabling and need factors. Analyses were carried out in the overall sample and restricted to those with moderate and severe symptoms (MSS) of depression and/or anxiety ( n = 2,237). Results: In individuals with MSS of depression and/or anxiety, 14.4% reported initiating new MHSU, 22.0% had no MHSU due to barriers and personal reasons and 36.7% had no MHSU due to self-reported no need. Age, living alone, lower income, a decrease in income during the pandemic and health professional status were associated with MHSU. Younger adults were more likely to initiate MHSU during the pandemic than older adults who reported not being comfortable to seek mental health care or self-reported no need. Individuals living alone and with lower income were more likely to report not being able to find an appointment for mental health care. Conclusions: Awareness campaigns focusing on older adults that explain the importance of seeking treatment is needed, as well as sensitising health professionals as to the importance of informing and aiding individuals at risk of social isolation and lower socio-economic status as to available mental health resources and facilitating access to care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".