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Enregistrement W2344493422 · doi:10.1111/hex.12470

Positive reporting? Is there a bias is reporting of patient and public involvement and engagement?

2016· editorial· en· W2344493422 sur OpenAlexaboutno aff
Carolyn Chew‐Graham

Notice bibliographique

RevueHealth Expectations · 2016
Typeeditorial
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPublic relationsPublic involvementPublic engagementService (business)Public serviceMedicinePsychologyMedical educationPolitical scienceBusinessMarketing

Résumé

récupéré en direct d'OpenAlex

Welcome to this edition of Health Expectations. As we have stated in earlier editorial briefings (e.g. 18.6), we are paying much more attention to the role played by patients and the public in selecting and agreeing the research question, study design and methods, interpretation and discussion of study findings, and in dissemination of results. So this edition of HEX particularly reflects this. In the UK, the National Institute for Health Research suggests that patients or service users can be involved in research in three ways, which are not mutually exclusive1: In Going the Extra Mile,2 a series of key recommendations are made, with plans for implementation, to further develop patient and public involvement in research. The report draws on the ‘strengths of the models of public involvement developed in Canada and the USA include their focus on communities and their assiduous attention to maintaining a clear line of sight from research design and delivery to patient outcomes and experience’. A key phrase in the report is on page 2: ‘Public Involvement should be so embedded in the culture (of NIHR) that new staff or new researchers coming into the field, would naturally take on the values and practices of effective public involvement’. Shippee et al.3 describe a model for the stages of patient and service user involvement and engagement: preparatory, execution and translational, and propose a framework which provides a standard structure and language for reporting and indexing to support comparative effectiveness and optimize patient and service user involvement. Tierney et al., in this edition of HEX, report their review of service user involvement in research and service development highlight that most studies only reported positive outcomes, raising questions about the balance or completeness of the published appraisals. They conclude that ‘to improve normalization of meaningful involvement in primary care, it is necessary to encourage explicit reporting of definitions, methodological innovation to enhance cogovernance and dissemination of research processes and findings’. Tierney et al. remind us of the PIRICOM Review4 which reported negative impacts on patients involved in research, in terms of personal impact, skill levels and knowledge levels, and users feeling overburdened, not listened to and marginalized. Fairbrother et al., in this edition of HEX, describe involving patients in a feasibility study using a ‘patient panel’ approach, but refer to their consideration of the word ‘scrutiny’ to describe the function of their panel. They report that involvement in the panel was considered a positive experience by participants, although ‘challenges were identified in terms of the time and cost implications of undertaking patient involvement’. Jinks et al.5 describe an on-going project which aims to describe and understand what the costs and consequences of patient and public involvement (PPI) in primary care research. This study has yet to report its findings, but a conference abstract indicates challenges in data collection.6 Boaz et al., in this edition of HEX, report a qualitative study exploring researchers’ attitudes to PPI and patient involvement in science (PES). They state that ‘while participants demonstrated a range of attitudes to these practices, they shared a resistance to sharing power and control of the research process with the public and patients’. This resonates with the difficulty Jinks et al.5 report in asking researchers to identify patient/service users and inviting them to complete questionnaires to generate data for their study. In a very recent article, Jinks et al.7 describe about how to sustain genuine PPIE involvement, beyond time-limited commitment to a single research project. They stress the need for institutional support and suggest that the following are needed: In conclusion, patient involvement and engagement is advocated, and indeed, most funding bodies demand it.1, 2 Attempts have been made to describe frameworks or models to conceptualize PPI; and while there is an increasing awareness of the challenges of PPI in high-quality research, as Tierney reports, there remains a positive bias in that most studies report positive outcomes for their PPI activities. We would like to encourage authors to report impact of PPI on studies in their submissions to HEX – and tell it how it is.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,212
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,257
Tête enseignante GPT0,466
Écart entre enseignants0,208 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

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

En bref

Citations7
Publié2016
Routes d'admission1
Résumé présentoui

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