Bayesian mediation analysis in trauma research.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Bayesian methods are growing in popularity among social scientists, due to the significant advantages offered to researchers: namely, intuitive probabilistic interpretations of results. Here, we highlight the benefits of using the Bayesian framework in research where collecting large samples is challenging, specifically: the absence of a requirement of large samples for convergence, and the possibility of building on prior research by including informative priors. METHOD: We demonstrate how to fit a single mediator model and impute missing data in the Bayesian framework using the software JAGS via the R package rjags. To this end, we use open-access data to fit a mediation model and calculate the posterior probability that the mediated effect is above a specified criterion. RESULTS: We replicate the results of the original paper in the Bayesian framework and provide annotated code for mediation analysis in rjags, as well as two additional R packages for Bayesian analysis (brms and rstan) and two additional software packages (SAS and Mplus). CONCLUSION: We provide guidelines for reporting and interpreting results obtained in the Bayesian framework, and two extensions to the mediation model are discussed: adding covariates to the model and selecting informative priors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.231 | 0.626 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.062 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it