End of Award Report for a Randomized Controlled Trial of Functional Family Therapy: An Early Intervention Foundation (EIF) Partnership between Croydon Council and Queen's University Belfast
Notice bibliographique
Résumé
The paper presents a closing report for the project ‘A Randomized Controlled Trial of Functional Family Therapy (FFT): An Early Intervention Foundation (EIF) Partnership between Croydon Council and Queen's University Belfast’. The work was supported from the United Kingdom Economic & Social Research Council/Early Intervention Foundation Grant Number ES/M006921/1. The grant was closed early due to the fact that staff at Croydon Council were unable to continue participation in the trial and complete their required work, as per the project plan. This prevented the randomization of the full sample and the subsequent planned analysis. The reasons for this were: 1. High staff turnover at Croydon, resulting in loss of key personnel. 2. Poor management of Children’s Services at Croydon. 3. Minimal referrals were made to the FFT team by Youth Offending Services (YOS) and Family Resilience Service (FRS). 4. Competition between ‘departments’ (YOS/FRS) was allowed to undermine the operational efficiency of the FFT team. 5. Changes in the management team at Croydon resulting in a senior manager being promoted and then overseeing the project from the point of failure, who had expressed open opposition to the randomized controlled trial at the outset of the research. This manager then made a decision to end Croydon’s participation almost immediately once they were in a position to do so. This displayed confusion over direction during this period of transition. 6. Children’s Services at Croydon was taken under direct Government control after an OFSTED report revealed ‘widespread and serious’ failures were leaving youngsters at risk during the lifetime of this project. It highlighted weak management in Croydon and a Commissioner was appointed to help Croydon improve. Recommendations and key learning There are lessons to be learned from this project. 1. It was possible to establish an effective working relationship and build and evaluation with a high degree of scientific integrity into the development of new initiatives within local authorities. There was an appetite in service delivery teams to contribute to the professional knowledge in their area of professional work. Other opportunities could be built into future specific calls, rather like the recent overseas aid calls. However, there would have to be a level of buy-in above middle management at local authority level to ensure projects can run to completion. 2. Local authorities appear to be developing complex and comprehensive data management systems for at risk families. These data management systems could be an invaluable tool in identifying at risk children and ensuring they get the help they deserve. For this to happen there needs to be synergy between data management personnel, managers and those delivering front line services in local authorities. The diagnostic ability of ‘big data’ may be worthy of future research. 3. There was not incompatibility between the ethical requirements of universities research review boards and the data protection requirements that both universities and local authorities work under. A method was found of securing and transferring data between Croydon and QUB. All are bound by the same legal framework and adherence to good working practices was evident in each institution. 4. If ESRC is funding collaborative grants between universities and local authorities, then it is important that prior to award a legally binding contract is obtained with the local authority. This should include penalty clauses should the local authority and the contract would need to be signed at Chief Executive level. This would be similar within a University where only the Vice-Chancellor and University Secretary/Registrar would usually make final decisions on acceptance. Good will and compromise on behalf of universities will not be enough to keep projects on track.
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 | 0,045 | 0,104 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,009 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,104 | 0,016 |
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 ».