Walk-in mental health: Bridging barriers in a pandemic
Bibliographic record
Abstract
'Single Session Therapy' (SST) is a service delivery model that seeks to provide an evidence-based, solution-focused, brief intervention within a single therapy session. The stand-alone session affords the opportunity to provide brief psychological interventions while clients await access to longer-term services. The COVID-19 pandemic has adversely impacted individuals' mental health. However, the majority of research has investigated patient mental health within hospital settings and community organizations that offer long-term services, whereas minimal research has focused on mental health concerns during COVID-19 within an SST model. The primary aim of the study was to measure client experiences of a brief mental health service. The nature of client mental health concerns who access such services at various points during a pandemic was also investigated. The current study utilized client feedback forms and the Computerized Adaptive Testing-Mental Health (CAT-MH) to measure client experiences and mental health concerns. Qualitative analysis of client feedback forms revealed themes of emotional (e.g., safe space) and informational support (e.g., referrals). Clients also reported reduced barriers to accessing services (e.g., no appointment necessary, no cost), as well as limitations (e.g., not enough sessions) of the Walk-in clinic. Profile analysis of the CAT-MH data indicated that clients had higher rates of depression before COVID-19 (M = 64.2, SD = 13.07) as compared to during the pandemic (M = 59.78, SD = 16.87). In contrast, higher rates of positive suicidality flags were reported during the pandemic (n = 54) as compared to before (n = 29). The lower reported rates of depression but higher rate of suicidality during the pandemic was an unanticipated finding that contradicted prior research, to which possible explanations are explored. Taken together, the results demonstrate the positive experiences of clients who access a single session therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".