RoB-SPEO: A tool for assessing risk of bias in studies estimating the prevalence of exposure to occupational risk factors from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury
Notice bibliographique
Résumé
The World Health Organization (WHO) and the International Labour Organization (ILO) are developing joint estimates of the work-related burden of disease and injury (WHO/ILO Joint Estimates). For this, systematic reviews of studies estimating the prevalence of exposure to selected occupational risk factors will be conducted to provide input data for estimations of the number of exposed workers. A critical part of systematic review methods is to assess risk of bias (RoB) of individual studies. In this article, we present and describe the development of such a tool, called the Risk of Bias in Studies estimating Prevalence of Exposure to Occupational risk factors (RoB-SPEO) tool; report results from RoB-SPEO's pilot testing; note RoB-SPEO's limitations; and suggest how the tool might be tested and developed further. Selected existing RoB tools used in environmental and occupational health systematic reviews were reviewed and analysed. From existing tools, we identified domains for the new tool and, if necessary, added new domains. For each domain, we then identified and integrated components from the existing tools (i.e. instructions, domains, guiding questions, considerations, ratings and rating criteria), and, if necessary, we developed new components. Finally, we elicited feedback from other systematic review methodologists and exposure scientists and agreed upon RoB-SPEO. Nine experts pilot tested RoB-SPEO, and we calculated a raw measure of inter-rater agreement (Pi) for each of its domain, rating Pi < 0.4 as poor, 0.4 ≤ Pi ≥ 0.8 as substantial and Pi > 0.80 as almost perfect agreement. Our review found no standard tool for assessing RoB in prevalence studies of exposure to occupational risk factors. We identified six existing tools for environmental and occupational health systematic reviews and found that their components for assessing RoB differ considerably. With the new RoB-SPEO tool, assessors judge RoB for each of eight domains: (1) bias in selection of participants into the study; (2) bias due to a lack of blinding of study personnel; (3) bias due to exposure misclassification; (4) bias due to incomplete exposure data; (5) bias due to conflict of interest; (6) bias due to selective reporting of exposures; (7) bias due to difference in numerator and denominator; and (8) other bias. The RoB-SPEO's ratings are low, probably low, probably high, high or no information. Pilot testing of the RoB-SPEO tool found substantial inter-rater agreement for six domains (range of Pi for these domains: 0.51–0.80), but poor agreement for two domains (i.e. Pi of 0.31 and 0.33 for biases due to incomplete exposure data and in selection of participants into the study, respectively). Limitations of RoB-SPEO include that it has not yet been fully performance-tested. We developed the RoB-SPEO tool for assessing RoB in prevalence studies of exposure to occupational risk factors. The tool will be applied and its performance tested in the ongoing systematic reviews for the WHO/ILO Joint Estimates.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,490 | 0,761 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,006 |
| Méta-épidémiologie (sens large) | 0,014 | 0,043 |
| Bibliométrie | 0,068 | 0,037 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,011 | 0,016 |
| Science ouverte | 0,007 | 0,023 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,038 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».