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Commentary on Chan MF, Yee ASW, Leung ELY &amp; 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

2007· letter· en· W2037786876 on OpenAlexaboutno aff
Anna Clarke

Bibliographic record

VenueJournal of Clinical Nursing · 2007
Typeletter
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionDiabetes mellitusIntervention (counseling)PopulationType 2 diabetesHealth careNursingGerontologyFamily medicine

Abstract

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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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.046
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.065
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0040.005
Scholarly communication0.0030.005
Open science0.0090.002
Research integrity0.0460.058
Insufficient payload (model declined to judge)0.0090.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.378
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2007
Admission routes1
Has abstractyes

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