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Enregistrement W2054115956 · doi:10.1111/hex.12293

Encouraging patient and public involvement in <scp>HEX</scp>

2014· editorial· en· W2054115956 sur OpenAlexaboutno aff
Carolyn Chew‐Graham

Notice bibliographique

RevueHealth Expectations · 2014
Typeeditorial
Langueen
DomaineHealth Professions
ThématiquePatient-Provider Communication in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPleaPublic relationsContext (archaeology)Reading (process)Political scienceAudience measurementPublic healthVocabularyStrengths and weaknessesWork (physics)PsychologyMedicineSocial psychologyNursingLaw

Résumé

récupéré en direct d'OpenAlex

Welcome to this edition of Health Expectations. The Editors have been discussing what we mean by our strapline and that HEX is ‘an International Journal of Public Participation in Health Care and Health Policy’. We are certainly international, attracting papers from around the world (in this issue, Canada, Israel, Spain, Taiwan as well as USA and UK) and have an international readership. However, there continues to be debate about what we really mean by publication participation. In the review article, Staley et al. reflect on the nature of the evidence that has been published to date, and explore the strengths and weaknesses of the different approaches that have been taken to evaluating the impact of public involvement on research. Using a realistic evaluation, the authors use two previously reported studies (exploring the impact of peer interviewers) and attempt to identify the links between context, mechanism and outcome in public involvement in research. Gagnon et al. make a plea for experimenting with different patient involvement strategies, and assessing their impact, to provide evidence that will inform future work, and highlighting the need to develop a common vocabulary. Table 3 in this paper makes useful reading to any researcher or policymaker planning patient and public involvement in their work. Chen et al. describe an interesting and perhaps unexpected consequence of increased involvement in decision-making – a tendency towards a higher risk of cancer death. The authors reflect on the limitations of this early study, but for clinicians, the challenge is how to ensure all relevant information about options are choices available is given to patients to support shared decision-making.1 Rapaport and colleagues illustrate the complexity of using a decision aid in the area of pre-natal testing, whilst attempting to achieve shared decision-making (SDM), which they describe as an iterative process, designed to find the best treatment for a specific patient through a better understanding of patient preferences by the physician and a better understanding of the medical situation by the patient. Using a decision aid, which is modelled on a biomedical approach and making certain presumptions about how people will behave, can challenge a SDM approach. The framework they present is clear and enables those of us in healthcare systems which are free to the patient at the point of delivery to recognize the extra layer of complexity patient charges present. Rochon and colleagues highlight a further barrier to the use of decision aids: the technology itself. In their paper, the participants were older people, but patients with low health literacy are likely to face similar problems in using decision aids2 or participating in health care about management of long-term conditions.3 Qualitative studies report patients’ views on splenectomy for idiopathic thrombocytopenic purpura, and the role of the community pharmacist, whilst an online survey showed that even academics may not understand what is meant by a ‘family history of cancer’. Such approaches shed light on the many and varied perspectives of patients and the public, and the value of exploring these perspectives in health care and policy. The breadth of the topics in this issue, the methodologies reported, and the implications for policy, practice and research, reflect the different ways in which researchers can explore the patient perspective, but we would like to take this further in our journal. Thus, the Editorial team would like to recruit PPI expertise on the Editorial Board and would encourage anyone who is interested, to contact the team for further discussion. We also wish to encourage short reports from Service User, Research User or Patient and Public Involvement (PPI) groups describing innovative ways in which patient and public involvement has either been researched or has impacted on health care, policy or research. We look forward to receiving your contributions.

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)0,001
score de la tête « metaresearch » (Gemma)0,010
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
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 enseignants0,201
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,010
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,004
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 Opus0,125
Tête enseignante GPT0,410
Écart entre enseignants0,286 · 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

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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