Registry · Priors

construct.safety_climate predicts construct.occupational_injuries

normal · informative · 3 studies · N = 19,395

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.259, -0.231]; mean ≈ -0.245.-0.274-0.259-0.245-0.231-0.216z0density

mean ≈ -0.245 · 95% CI ≈ [-0.259, -0.231]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.253, -0.227]; mean ≈ -0.240.-0.267-0.254-0.240-0.227-0.213r0density

mean ≈ -0.240 · 95% CI ≈ [-0.253, -0.227]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.25, -0.23]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.24, -0.24]
I² (heterogeneity) — share of total variance from between-study differences
0% — studies largely agree

The true effect is ~constant across settings — it generalizes.

SD_ρ≈0 — true effect is ~constant across settings; generalizes (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=-0.24 (k=3, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.24k = 3 · N = 19,395

No primary-deployment evidence yet — this prior rests on published literature alone. As anonymized, aggregated effect sizes from real deployments are contributed, they appear here as a distinct, publication-bias-free source, fused with the literature into a posterior estimate.

Code

Drop this prior straight into your model. Snippets generated from the synthesized distribution + parameters.

target += normal_lpdf(beta | -0.24489, 0.00716814);
beta = pm.Normal("beta", mu=-0.24489, sigma=0.00716814)
brms::prior(normal(-0.24489, 0.00716814), class = "b")
# base R sample
rnorm(N, mean = -0.24489, sd = 0.00716814)
np.random.normal(loc=-0.24489, scale=0.00716814, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = -0.2449, sigma = 0.007168, r_mean = -0.2401, k_studies = 3.000, tau_squared = 0.000, fisher_z_bias = -0.00005459, r_mean_bare_bones = -0.2401

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
3
n_total
19,395
Last updated
2026-07-16T01:08:22.280Z

Quality distribution

GradeCount
A2
B1
C0
D0

Source articles

The research this prior is synthesized from — each is a full dossier (findings, the models it informs, and what the literature says).

Contributing effect sizes

Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.