Acceptability of Internet-based interventions for problem gambling: a qualitative study of focus groups with clients and clinicians
Bibliographic record
Abstract
Abstract Background Although Internet-based interventions (IBIs) have been around for two decades, uptake has been slow. Increasing the acceptability of IBIs among end users may increase uptake. In this study, we explored the factors that shape acceptability of IBIs for problem gambling from the perspective of clients and clinicians. Findings from this qualitative study of focus groups informed the design and implementation of an IBI for problem gambling.Methods Using a semi-structured interview guide, we conducted three focus groups with clients experiencing gambling problems (total n = 13) and two with clinicians providing problem gambling treatment (total n = 21). Focus groups were audio recorded, transcribed verbatim, and analyzed using a two-part inductive-deductive approach to thematic analysis.Results Although both user groups reported similar experiences, each group also had unique concerns. Clinician perspectives were more homogeneous reflective of healthcare professionals sharing the same practice and values. Clinicians were more concerned about issues relating to the dissemination of IBIs into clinical settings, including the development of policies and protocols and the implications of IBIs on the therapeutic relationship. In comparison, client narratives were more heterogeneous descriptive of diverse experiences and individual preferences, such as the availability of services on a 24-hour basis. There was consensus among clients and clinicians on common factors influencing acceptability: access, usability, high quality technology, privacy and security, and the value of professional guidance.Conclusions Acceptability is an important factor in the overall effectiveness of IBIs. Gaining an understanding of how end users perceive IBIs and why they choose to use IBIs can be instrumental in the successful and meaningful design, implementation, and evaluation of IBIs.
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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 | 0.035 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".