Identification of Complex Health Interventions Suitable for Evaluation: Development and Validation of the 8-Step Scoping Framework
Notice bibliographique
Résumé
BACKGROUND: There is extensive literature on the methodology of evaluation research and the development and evaluation of complex interventions but little guidance on the formative stages before evaluation and how to work with partner organizations that wish to have their provision evaluated. It is important to be able to identify suitable projects for evaluation from a range of provision and describe the steps required, often with academic institutions working in partnership with external organizations, in order to set up an evaluation. However, research evaluating programs or interventions rarely discusses these stages. OBJECTIVE: This study aimed to extend work on evaluability assessment and pre-evaluation planning by proposing an 8-Step Scoping Framework to enable the appraisal of multiple programs in order to identify interventions suitable for evaluation. We aimed to add to the literature on evaluability assessment and more recent evaluation guidance by describing the processes involved in working with partner organizations. METHODS: This paper documents the steps required to identify multiple complex interventions suitable for process and outcome evaluation. The steps were developed using an iterative approach by working alongside staff in a local government organization, to build an evidence base to demonstrate which interventions improve children's outcomes. The process of identifying suitable programs for evaluation, thereby establishing the pre-evaluation steps, was tested using all Flying Start provision. RESULTS: The 8-Step Scoping Framework was described using the example of the local government organization Flying Start to illustrate how each step contributes to finding projects suitable for process and outcome evaluation: (1) formulating overarching key questions that encompass all programs offered by an organization, (2) gaining an in-depth understanding of the work and provision of an organization and engaging staff, (3) completing a data template per project/program offered, (4) assessing the robustness/validity of data across all programs, (5) deciding on projects suitable for evaluation and those requiring additional data, (6) negotiating with chosen project leads, both within and outside the organization, (7) developing individual project evaluation protocols, and (8) applying for ethical approval from the university and partner organization. CONCLUSIONS: This paper describes the processes involved in identifying suitable projects for evaluation. It adds to the existing literature on the assessment of specific programs suitable for evaluation and guidance for conducting evaluations by establishing the formative steps required to identify suitable programs from a range of provision. This scoping framework particularly relates to academic partners and organizations tasked with delivering evidence-based services designed to meet local needs. The steps identified have been described in the context of early years provision but can be applied to a range of community-based evaluations, or more generally, to cases where an academic partner is working with external stakeholders to identify projects suitable for academic evaluation.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,038 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 tête enseignante, 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 ».