Notice bibliographique
Résumé
Is QA investment worth it?The evidence is familiarscreening the population and preventing ill health and healthcare complications, especially in the young can save billionsbut at what personal and economic cost to service providers?Doug Ford and Dick Zoutman in this issue explore quality improvement (QI) success and failure from an employee perspective.Their on-line survey measured QI project time, effort, commitment, reward, benefits and downsides in Canadian acute hospitals.Survey response rates, despite follow-up e-mails, were disappointing, which rings warning bells about QI project commitment and engagement.On the upside, however, data were gathered from 125 acute hospitals, which improves external validity (the extent to which findings can be generalised).Encouragingly, respondents, despite their negativity, felt that QI projects improved patient safety and service quality.Also encouraging were the hospital QI projects' breadth and depthnotably how intractable healthcare problems (such as hospital acquired infections) were being solved.The major downsides included competition between QI project demands and healthcare professionals' clinical duties.That is, worryingly, it is the key stakeholders (nurses and doctors) most affected by these competing demands and, therefore unsurprisingly, are the less committed professionals.Surprisingly, QI education and training programmes were not universally supported.Manager and leader commitment, on the other hand, featured in bucket loads.Clearly, the challenge is to study how best to support and encourage clinicians to become more engaged with QI.Chinweike Eseonu and colleagues also underline successful QI project structures and processes.They explore QI project drivers and barriers in North America.Although using an unusual theoretical framework and a more triangulated (quantitative and qualitative) approach than Ford and Zoutman, Eseonu et al., unearth similar outcomes.Their respondents also believed that QI projects improved service delivery, but felt aggrieved that insufficient time and resources were provided for CI work and how professionals were expected to deliver QI and clinical goals simultaneouslyalso the Ford and Zoutman study respondents' biggest gripe.The Eseonu et al., study participants' main grievance, however, unlike Ford and Zoutman, was managerial commitment to and support for QI projects.Both studies share common ground, but it is clear that ventures in different contexts need bespoke approaches if they are to be successful and sustainable.One scenario possibly worse than maintaining unpopular QI projects is not knowing what quality is like because service provision is unmeasured.Health and social care permutations mean that there always will be services where quality has not been measured or where quality assurance data are too old; so the adage that we should not change anything that has not been measured applies.In this issue, Vigdis Grøndahl and Liv Fagerli measure Norwegian nursing home service quality is some detaila challenging research and development (R&D) topic owing to residents' questionable mental capacity.Their cluster analysis reveals a significant elderly group who are dissatisfied with many services.Clearly, the inter-relationships between service domains in the negative elderly resident cluster are complex, which presents managers with a challenge.The growing elderly population, most having made significant contributions to their country, with increasing co-morbidities, mean that they are a sector deserving the best care and service monitoring that can be mustered.It does not matter whether the organisation in which we work is large or small, one irritation is not being able to find a file or documenta problem in hospitals, which is dangerous.Records control, therefore, is paramount and it is little wonder that records departments are being accredited and certificated using ISO standards.Owing to the myriad
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 | 0,003 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,224 | 0,109 |
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 ».