Comparing Brief Internet-Based Compassionate Mind Training and Cognitive Behavioral Therapy for Perinatal Women: Study Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Depression that occurs during the perinatal period has substantial costs for both the mother and her baby. Since in-person care often falls short of meeting the global need of perinatal women, Internet interventions may function as an alternate to help women who currently lack adequate access to face-to-face psychological resources. However, at present there are insufficient empirically supported Internet-based resources for perinatal women. OBJECTIVE: The aim of this study is to compare the relative efficacy of Internet-based cognitive behavioral therapy (CBT) to a novel Internet-based compassionate mind training approach (CMT) across measures of affect, self-reassurance, self-criticizing, self-attacking, self-compassion, depression, and anxiety. While CBT has been tested and has some support as an Internet tool for perinatal women, this is the first trial to look at CMT for perinatal women over the Internet. METHODS: Participants were recruited through Amazon Mechanical Turk (MTurk) and professional networks. Following completion of demographic items, participants were randomly assigned to either the CBT or CMT condition. Each condition consisted of 45-minute interactive didactic and follow-up exercises to be completed over the course of two weeks. RESULTS: Post course data was gathered at two weeks. A 2x2 repeated measures analysis of variance will be conducted to analyze differences between conditions at post course. CONCLUSIONS: The implications of the trial will be discussed as well as the strengths and limitations of MTurk as a tool for recruitment. We will also briefly introduce the future directions along this same line of research. TRIAL REGISTRATION: ClinicalTrials.gov NCT02469324; https://clinicaltrials.gov/ct2/show/NCT02469324 (Archived by WebCite at http://www.webcitation.org/6fkSG3yuW).
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.080 | 0.013 |
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".