Exploring client messages in a therapist-guided internet intervention for alcohol use disorders – A content analysis
Bibliographic record
Abstract
There is a growing interest in offering therapist-guided internet interventions for alcohol use disorders (AUD) in regular addiction services. Elucidating the therapeutic processes during these interventions may help improve clinical delivery. The aim of this paper was to investigate written messages from client to therapist in a therapist-guided internet intervention for AUD. Data was extracted from the therapist-guided arm (n = 57) of a randomized trial of internet interventions for AUD. Qualitative content analysis was used to identify distinct categories of client behaviors in written messages to therapists. Coding was deductive (applying categories from past literature) as well as inductive (identifying new categories from the data). Subsequently, exploratory correlational and regression analyses were conducted to investigate whether identified client behaviors predicted module completion and drinking outcomes. Also, client questions posed in messages to therapists were categorized separately. Eleven distinct behavior categories were identified, of which the two most common were alliance (26.6% of total categorizations) and identifying patterns and problem behaviors (22.8%). Confrontational alliance rupture was the least common category (0.4%). One new behavior category was identified inductively – alcohol-related setback (4.1%). In the exploratory analyses, no categories consistently predicted module completion or drinking outcomes. Client questions were most commonly posed to improve understanding or use of program content or skills. The behavior categories, although not predictive of module completion or outcomes, may be of use for therapists, treatment developers and health care providers as a tool for understanding therapeutic processes in internet interventions for AUD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".