Commentary on Chan MF, Yee ASW, Leung ELY & Day MC (2006) The effectiveness of a diabetes nurse clinic in treating older patients with type 2 diabetes for their glycaemic control. <i>Journal of Clinical Nursing</i> 15, 770–781
Notice bibliographique
Résumé
Diabetes mellitus is now accepted as the major pandemic of the 21st century with seniors more disproportionately affected, and it is projected to become one of the world's main disablers and killers within the next 25 years. Current health services are responding to the increasing number of patients in the traditional acute care model. Ultimately, this means shorter and shorter consultations with reduced time for diabetes education (McGill 2005). The result is an explosion of nurse-led clinics with much research focusing on attaining scientific support for nursing interventions. Indeed, given the trend towards cost-containment in health services and the increasing numbers of newly diagnosed people with diabetes type 2, the development of nurse-led clinics is inevitable. The findings of Chan et al.’s (2006) study provide evidence of the improvement of medical outcomes for a group of 75 people with diabetes mellitus type 2 in Hong Kong. However, before the findings of the study can be applied to clinical practice, the merits and the conclusions of the study require commentary. The focus of this commentary will be twofold: the implication that the intervention was beneficial to the patients and the methodology. In the present study, ‘elderly’ was operationally defined as over 65 years, while ‘poor glyceamic control’ was operationally defined as having a HbA1c of >8·5%. Two difficulties arise when accepting these definitions. Defining elderly according to chronological age has limited significance in health. Defined in such terms, the elderly represents a heterogeneous population ranging from frail to active individuals with variable life expectancies. Life expectancy is the basis for setting the target of glycaemia control. Because of increases in the general health of people in their 60s, it is now more common to define elderly as being over 70 years of age (Hiltunen 2005, Millionis et al. 2005). The Diabetes Control and Complications Trial (1993) and the UK Prospective Diabetes Study Group (1998) are the two principal studies that provided evidence, which gave rise to the target HbA1c of <7%. Prandial swings and marked hyperglycaemia after meals are important indicators of glyceamic control, but these indicators are poorly captured by the HbA1c. These indicators are of particular relevance for the target population in Chan et al.’s study because of their contribution to cardiovascular risk, which is the major contributor to mortality and morbidity in the age group of concern to the study. In addition, there is an evidence that glucose levels increase with age (Motta 2006) and, therefore, when targeting older people, the benefits of tight glyceamic control must be weighed against the risk of adverse hypoglycaemic effects. The older population is less likely to benefit from reducing the risk of micro vascular complications and more likely to suffer serious adverse effects from hypoglycaemia (Tarannum 2005). A mean reduction in HbA1c of 0.8% over a three-month period warrants further exploration in relation to quality of life. Indeed, the improved glycaemic control may be due as much to frequent healthcare contact and subsequent medication adherence as to the actual intervention. The present study also requires clarification of some methodological aspects. Response rates are generally considered to be the most widely compared statistic for judging the quality of surveys (Biemer & Lyberg 2003). In Chan et al.’s study, aside from the assertion that no one was lost in follow-up, the response rate is not reported. The implication of this is that the response rate was 100%, which seems to be not credible, and requires clarification. It is generally accepted that response rates are declining because of factors outside the control of the researcher (Groves et al. 2002). This need not be a problem provided there is full disclosure along with an indication of the measures used to promote high response levels and comparison of non-responders to responders. The authors also do not indicate how the scores were distributed for HbA1c, weight and PEQD. Reporting of the skewness and shape of the distribution would permit a determination as to whether the decision not to transform the data was indeed the correct one (Pett 1997). If the older population was a heterogeneous group, it would be expected that both groups in the study would assume similar distribution and, therefore, a transformation could be computed to meet the normality assumption. The use of non-parametric tests puts emphasis on the rank ordering and frequencies of the data. The collapsing of the PEQD scale from five points to four may have further compromised the robustness of the tests used. Notwithstanding the aforementioned limitations, the use of Hibbard's model is noteworthy in that it enhances the patient's role in diabetes care and makes the patient's perceptions available for critical analysis. Client empowerment is essential if they are to assume the lead role in the diabetes team. Hibbard's model supports empowerment by promoting informed choice by patients, including the principle of patients as co-producers of their care, and facilitating patients to voice their views on the care that they receive. Chan et al.’s study adds to our knowledge of the effects of nurse-led diabetes clinics in a Hong Kong setting. Further research is warranted to determine quality of life effects of improved glyceamic control in this population.
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 | 0,009 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,009 | 0,002 |
| Intégrité de la recherche | 0,046 | 0,058 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,012 |
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 ».