Partnering with survivors & families to determine research priorities for adult out-of-hospital cardiac arrest: A James Lind Alliance Priority Setting Partnership
Notice bibliographique
Résumé
BACKGROUND: Research priority setting in health care has historically been done by expert health care providers and researchers and has not involved patients, family or the public. Survivors & family members have been particularly absent from this process in the field of resuscitation research and specifically adult out of hospital cardiac arrest (OHCA). As such, we sought to conduct a priority setting exercise in partnership with survivors, lay responders and their families in order to ensure that their priorities were visible. We partnered with the James Lind Alliance (UK) and used their commonly used consensus methodology for Public Priority Setting Partnerships (PSPs) to identify research priorities that reflected the perspectives of all stakeholders. METHODS: We used two rounds of public and health care professional surveys to create the initial priority lists. The initial survey collected open-ended questions while the second round consolidated the list of initial questions into a refined list for prioritization. This was done by reviewing existing evidence and thematic categorization by the multi-disciplinary steering committee. An in-person consensus workshop was conducted to come to consensus on the top ten priorities from all perspectives. The McMaster PPEET tool was used to measure engagement. RESULTS: The initial survey yielded more than 425 responses and 1450 "questions" from survivors and family members (18%), lay responders, health care providers and others. The second survey asked participants to rank a short list of 125 questions. The final top 25 questions were brought to the in-person meeting, and a top ten were selected through the JLA consensus process. The final list of top ten questions included how to improve the rate of lay responder CPR, what interventions used at the scene of an arrest can improve resuscitation and survival, how survival can be improved in rural areas of Canada, what resuscitation medications are most effective, what care patient's family members need, what post-discharge support is needed for survivors, how communication should work for everyone involved with a cardiac arrest, what factors best predict neurologically intact survival, whether biomarkers/genetic tests are effective in predicting OHCA and more research on the short and long-term psycho-social impacts of OHCA on survivors. The PPEET showed overwhelmingly positive results for the patient and family engagement experience during the final workshop. CONCLUSIONS: This inclusive research priority setting provides essential information for those doing resuscitation research internationally. The results provide a guide for priority areas of research and should drive our community to focus on questions that matter to survivors and their families in our work. In particular the Canadian Resuscitation Outcomes Consortium will be incorporating the top ten list into its strategic plan for the future.
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 | 0,142 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,017 | 0,006 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,002 | 0,028 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».