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Enregistrement W3082649971 · doi:10.3310/hsdr08330

Publication and related bias in quantitative health services and delivery research: a multimethod study

2020· article· en· W3082649971 sur OpenAlexfundno aff
Abimbola Ayorinde, Iestyn Williams, Russell Mannion, Fujian Song, Magdalena Skrybant, Richard Lilford, Yen‐Fu Chen

Notice bibliographique

RevueHealth Services and Delivery Research · 2020
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute for Health Research Applied Research Collaboration WestNational Institutes of HealthHealth and Social Care Delivery ResearchUniversity of SouthamptonHealth Services and Delivery Research ProgrammeNational Institute for Health and Care ResearchMcMaster University
Mots-clésPublication biasSystematic reviewService delivery frameworkHealth services researchMedicineReporting biasEmpirical researchPsychologyPublic healthMEDLINEApplied psychologyMeta-analysisService (business)NursingMarketingBusinessPolitical scienceStatistics

Résumé

récupéré en direct d'OpenAlex

Background Bias in the publication and reporting of research findings (referred to as publication and related bias here) poses a major threat in evidence synthesis and evidence-based decision-making. Although this bias has been well documented in clinical research, little is known about its occurrence and magnitude in health services and delivery research. Objectives To obtain empirical evidence on publication and related bias in quantitative health services and delivery research; to examine current practice in detecting/mitigating this bias in health services and delivery research systematic reviews; and to explore stakeholders’ perception and experiences concerning such bias. Methods The project included five distinct but interrelated work packages. Work package 1 was a systematic review of empirical and methodological studies. Work package 2 involved a survey (meta-epidemiological study) of randomly selected systematic reviews of health services and delivery research topics ( n = 200) to evaluate current practice in the assessment of publication and outcome reporting bias during evidence synthesis. Work package 3 included four case studies to explore the applicability of statistical methods for detecting such bias in health services and delivery research. In work package 4 we followed up four cohorts of health services and delivery research studies (total n = 300) to ascertain their publication status, and examined whether publication status was associated with statistical significance or perceived ‘positivity’ of study findings. Work package 5 involved key informant interviews with diverse health services and delivery research stakeholders ( n = 24), and a focus group discussion with patient and service user representatives ( n = 8). Results We identified only four studies that set out to investigate publication and related bias in health services and delivery research in work package 1. Three of these studies focused on health informatics research and one concerned health economics. All four studies reported evidence of the existence of this bias, but had methodological weaknesses. We also identified three health services and delivery research systematic reviews in which findings were compared between published and grey/unpublished literature. These reviews found that the quality and volume of evidence and effect estimates sometimes differed significantly between published and unpublished literature. Work package 2 showed low prevalence of considering/assessing publication (43%) and outcome reporting (17%) bias in health services and delivery research systematic reviews. The prevalence was lower among reviews of associations than among reviews of interventions. The case studies in work package 3 highlighted limitations in current methods for detecting these biases due to heterogeneity and potential confounders. Follow-up of health services and delivery research cohorts in work package 4 showed positive association between publication status and having statistically significant or positive findings. Diverse views concerning publication and related bias and insights into how features of health services and delivery research might influence its occurrence were uncovered through the interviews with health services and delivery research stakeholders and focus group discussion conducted in work package 5. Conclusions This study provided prima facie evidence on publication and related bias in quantitative health services and delivery research. This bias does appear to exist, but its prevalence and impact may vary depending on study characteristics, such as study design, and motivation for conducting the evaluation. Emphasis on methodological novelty and focus beyond summative assessments may mitigate/lessen the risk of such bias in health services and delivery research. Methodological and epistemological diversity in health services and delivery research and changing landscape in research publication need to be considered when interpreting the evidence. Collection of further empirical evidence and exploration of optimal health services and delivery research practice are required. Study registration This study is registered as PROSPERO CRD42016052333 and CRD42016052366. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research ; Vol. 8, No. 33. See the NIHR Journals Library website for further project information.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,426
score de la tête « metaresearch » (Gemma)0,575
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Présentation des résultats · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,574
Score d'incertitude au seuil0,708

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,4260,575
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0080,011
Bibliométrie0,0280,031
Études des sciences et des technologies0,0040,006
Communication savante0,0100,009
Science ouverte0,0040,010
Intégrité de la recherche0,0050,002
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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.

Tête enseignante Opus0,526
Tête enseignante GPT0,479
Écart entre enseignants0,047 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
DomainePrésentation des résultats
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2020
Routes d'admission1
Résumé présentoui

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