Measuring Successes of Social Prescribing
Notice bibliographique
Résumé
Social prescribing is a referral mechanism which connects people to non-medical, community and social based activities, which aim to empower an individual to take control of and manage their health and wellbeing (Husk et al., 2020). It has experienced rapid global growth in recent years (Morse et al., 2022) and has been recognised to have the potential to address individual, social and societal determinants of health, by improving access to adequate social support, adequate housing, and financial support, in order to avoid social isolation and loneliness for people (NHS England, 2020). While there is obvious growth in social prescribing services, and clear targets for the implementation of social prescribing, there is a reported lack of evaluation of the services, with recent research concluding that economic evaluation of social prescribing is weak, with limited research and evidence in evaluating the impact of social prescribing (Kiely et al., 2022). This is echoed in communities delivering social prescribing, where difficultly in evaluating social prescribing, along with inadequate evaluation processes have been reported (Mulholland, Galway and Lindsay, 2022). The rapid growth of Social Prescribing has resulted in a need for effective, robust evaluation processes, to determine the impact of social prescribing on the health of individuals, as well as its impact on local communities and associated financial costs. \n \nThis report shares details of a one-day workshop on ?Measuring Successes of Social Prescribing? held in Trinity College Dublin in June 2023. The workshop was hosted by the Research and Evaluation Committee of the All-Ireland Social Prescribing Network (AISPN) and was facilitated by Ms. Pat Tobin from Community Action Network (CAN). It was organised as a follow up to a short break out session held the previous year at the All-Ireland Social Prescribing Network (AISPN) conference, held in 2022. During the conference break out session, delegates were invited to share their experience of outcome measurement in social prescribing, incorporating views from link workers, service managers, funders and academic researchers. This immensely informative event provided insights on the challenges associated with measuring and evaluating social prescribing, and it was clear that there was demand for more debate in this area. A conference report is available here. As a result of the interest in further developing measurement strategies, funding was sought to host a one-day workshop. \n \nThe purpose of the workshop was to bring together those involved in social prescribing across the island of Ireland, to discuss the evaluation of social prescribing, and to make recommendations that would result in improving evaluation processes. With funding support acquired from the Health Research Board in Ireland and the Public Health Agency in Northern Ireland, over 60 attendees were able to take part in the workshop, including social prescribing service users, link workers, social prescribing co-ordinators and funders.
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 | 0,025 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».