Meet Your Public: A new Internet-delivered Program Integrating Exposure Therapy and Self-compassion Writing Exercises May Lessen Fear of Public Speaking while Increasing Self-compassion
Bibliographic record
Abstract
Fear of public speaking (FoPS) is a prevalent condition that remains undertreated despite exposure being an efficient intervention. Internet-based exposure therapy may facilitate access to treatment, but engagement can be a challenge. Internet-based written exposure therapy combined to selfcompassion training may help to improve FoPS and engagement. This study assessed the feasibility of a minimally guided internet-based written exposure and self-compassion therapy for FoPS. Meet Your Public. It is a 6-week program available in English and French that includes psychoeducation and writing exercises related to FoPS, exposure and self-compassion. Nineteen participants were eligible for analysis. A single group design including a 3-month follow-up was used. Feasibility outcomes included adherence, attrition, treatment acceptability and preliminary efficacy on FoPS, negative and positive self-statements pertaining to FoPS, and self-compassionate and self-uncompassionate behaviors. About two thirds of the participants completed the program and the study (63%). Most study completers reported that they would recommend the program to a friend (80%). Intent-to-treat mixed-effect models analyses revealed large improvements of FoPS (Glass’ delta= 1.23) which were maintained at follow-up. Small to moderate improvements were also found from pre-treatment to post-treatment or follow-up on all other outcome measures (Glass’ deltas from 0.21 to 0.68). Meet Your Public may be beneficial while facilitating access to treatment for FoPS. Future directions to further improve engagement and satisfaction are discussed
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".