Case Stories in Internet-Delivered Cognitive Behavioral Therapy for Public Safety Personnel: A Mixed-Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Internet-delivered cognitive behavioral therapy (ICBT) is an effective and convenient means of offering cognitive behavioral therapy among the general population. To help increase access to ICBT among Canadian public safety personnel (PSP)—a group that tends to experience elevated rates of mental health concerns and faces barriers to mental healthcare—a clinical research unit called PSPNET has tailored ICBT to PSP, primarily through offering case stories and PSP-specific examples. PSPNET’s first and most frequently used ICBT program, called the PSP Wellbeing Course, has been found to reduce symptoms of mental disorders (eg, anxiety, depression, posttraumatic stress) among PSP. Little research, however, has investigated clients’ perceptions of the case stories in this course. OBJECTIVE The current study was designed to expand literature on the use and evaluation of case stories in ICBT among PSP. Specifically, the current study investigated: (1) PSP’s perceptions of the case stories using the theoretical model provided by Shaffer and Zikmund-Fisher [21]; and (2) PSP feedback on the case stories in the PSP Wellbeing Course. METHODS The current study included 41 clients who completed the PSP Wellbeing Course. Of these clients, 27 completed a bespoke questionnaire called the Stories Questionnaire, 10 of whom also participated in a semi-structured interview. RESULTS Findings show that perceptions of the case stories in the PSP Wellbeing Course are largely positive and that the case stories were generally successful in achieving the five purposes of case stories (ie, informing, comforting, modeling, engaging, and persuading) proposed by Shaffer and Zikmund-Fisher [21]. Client feedback also identified three tangible areas for story improvement: characters, content, and delivery. Each area highlights the need for and potential benefits of story development. Not all PSP engaged with the case stories, though, so results must be interpreted with caution. CONCLUSIONS Overall, the current study adds to the growing body of research supporting the use of case stories in internet-delivered interventions among PSP. CLINICALTRIAL Clinicaltrials.gov NCT04127032
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".