The adaptation to COVID-19 by problem gambling and mental health treatment providers in Canada: a brief report.
Bibliographic record
Abstract
Background: During the Covid-19 pandemic, online gambling venues remained accessible while treatment services were met with constraints. Mental health service providers needed to adapt quickly to continue supporting clients. This exploratory study examined the experiences of problem gambling counsellors and other treatment professionals who worked throughout the Covid-19 pandemic in terms of (1) how they were impacted by the pandemic, (2) about how they adapted to the pandemic, and (3) their training needs in order to be better prepared for future pandemics. Method: Counsellors in Canada were surveyed using closedand open-ended questions. The study was conducted in two waves, one in May to July 2021 in the middle of the pandemic, and the second from April to June 2022 as many public health restrictions were being removed and the casinos were being reopened. Results: The results indicated increases in counsellor distress during the pandemic. In addition, the counsellors also reported increased stress in their clients. The participants reported a shift towards phone and online treatment during the pandemic but also expressed a need for additional training on remote counselling methods. The counsellors reported concerns over technological issues, privacy issues and problems with keeping clients engaged. There were also concerns regarding populations who do may not have access to technology such as homeless people and seniors. Conclusions: There is a need for research to define best practices for remote methods of counselling.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".