Audit and feedback of therapist-assisted internet-delivered cognitive behaviour therapy within routine care: A quality improvement case study
Bibliographic record
Abstract
With the growing use of ICBT in routine care clinics there is a need for literature on how to monitor and improve the quality of therapist behaviours in clinical practice. In this paper, we first provide background literature on Audit and Feedback (A&F), a common quality improvement technique, and then present a case study regarding the use of A&F to improve quality of therapist behaviours in emails sent to patients provided with ICBT in routine care. The A&F measure used was derived from previous research on therapist's email behaviours in ICBT. Fifteen undesirable therapist behaviours (e.g., Did Not Message, Unresponsive to Symptom Increase, Does Not Address Patient Concern) were audited in 1840 emails sent from eight therapists to 198 randomly selected patients, representing 18% of 1114 patients who started between one and five lessons of ICBT in the previous year and did not formally withdraw from treatment (n = 31 patients). The therapists who were audited were provided feedback four times over a one-year period from October 2018 to September 2019. Overall, in all audit periods, we found a low percentage of undesirable therapist behaviours (i.e., therapists displayed the behaviour in 12% or less of the total emails sent). For most therapist behaviours, we saw a trend towards improvement across the four audit cycles. Three therapist behaviours (i.e., Failure to Ask One Question to the Patient, Poor Instructions, Not Linking Email to Course Content) did not follow this pattern and were flagged for clinical discussion to determine why behaviours were elevated and whether these behaviours represented unrealistic expectations. The process was valuable for monitoring and improving therapist behaviours and highlights the need for future research on standards for therapist behaviours (e.g., which behaviours to focus on, setting acceptable levels of undesirable behaviour).
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".