Pandemic impacts on problem gambling treatment providers
Bibliographic record
Abstract
During the Covid-19 pandemic, online gambling venues remained accessible while treatmentservices were met with constraints. Mental health service providers needed to adapt quicklyto continue supporting clients. This study examined how services for people who have gamblingand other mental health problems adapted treatment during the Covid-19 pandemic.Counsellors from two provinces in Canada were surveyed using closed- and open-endedquestions. The study was conducted in two waves, one in May to July 2021 in the middleof the pandemic, and the second from April to June 2022 as many public health restrictionswere being removed and casinos reopened. Questions included how counsellors adapted theirpractice during the pandemic and what training they felt they required to help them deliversafe and effective treatment during a pandemic. The results indicated increases in counsellordistress during the pandemic. The counsellors also reported increased stress in their clients.The participants reported a shift towards phone and online treatment during the pandemic.The counsellors in this study had concerns over technological issues, privacy issues andproblems with keeping clients engaged. There were also concerns regarding populations whodo not have access to remote treatment methods and vulnerable populations such as seniors,Indigenous groups, and people who have serious dual diagnoses. There is a need for futurepreparation in mental health treatment protocols to mitigate shortfalls in remote client 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".