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Record W4283327466 · doi:10.1111/jan.15272

Assessments of nursing practice: The role of medico‐economic analysis

2022· letter· en· W4283327466 on OpenAlexaboutno aff
Charline Mourgues, Alexandra Usclade

Bibliographic record

VenueJournal of Advanced Nursing · 2022
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMedicineNursing Interventions ClassificationRandomized controlled trialNursingMeta-analysisIntervention (counseling)Cost–benefit analysisMEDLINEEconomic evaluationFamily medicineIntensive care medicineSurgery

Abstract

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Your journal published the following article in January 2022: Nurse-led interventions to manage hypertension in general practice: A systematic review and meta-analysis (Stephen et al., 2022), which evaluated the impact of current nursing practices on blood pressure control and risk factors of cardiovascular disease reduction for hypertensive patients. In this meta-analysis, the authors showed the GPN-led interventions to be heterogeneous and potentially favourable in risk prevention of targeted patients, but that research must be pursued to determine factors that positively influence this result and to assess its cost-effectiveness. We would like to discuss the latter point. The authors highlighted that patient satisfaction assessment and economic analyses were missing from the 11 studies retained for the meta-analysis and represented two multifactorial and nuanced variables that could impact the evaluation of nursing interventions. However, of these 11 selected articles, one (Bosworth et al., 2009) included economic elements which were not developed by the authors. This is the only paper that incorporated a cost-per-patient analysis of nursing interventions. The results of this randomized controlled trial indicate costs per patient over the 2 years of the study of $90 for home blood pressure monitoring, $345 for nursing intervention (i.e. ‘bi-monthly nurse administered behavioural self-management intervention’) and $416 for the combined intervention (nurse and home monitoring), not including the time spent by patients. No difference was recorded in the number of hospitalizations. Calculated costs in this study are direct medical costs (nursing time and medical equipment). It is to the authors' credit that financial issues were added to the clinical arguments, which of course take priority in care, but we regret that tools of economic disciplines are not more widely used in health programme assessment. In the Bosworth et al. (2009) article, it would have been advisable to explain these costs in relation to clinical outcomes so as not to discriminate between different interventions on clinical efficacy outcomes only but on a cost- effectiveness ratio, which represents a more complex but comprehensive indicator for optimal health choice decisions in a context of limited physical and human resources. Currently, insufficient use is made of medico-economic analyses in assessments of nursing practice, in particular, due to the complexity of tools, which are not sufficiently qualitative. It is difficult to decide clearly and precisely between several interventions. Marshall et al. (2015), in a literature review that assessed the quality of cost-effective economic studies of nursing practices, also made this statement and reported a recurrent lack of analysis and control of uncertainty in economic models. Of the 43 studies incorporated in their literature review, only three were classified as high-quality studies. In addition, economic analysis is still not routinely included in the university curriculum for nurses and a certain distrust remains palpable about the economic discipline because it is too often perceived as an obstacle and not as a decision-making tool (Caniard, 2015). According to the same authors, it is not easy to sell an economic assessment with an apparent gap between ‘tools for ambitious goals and sometimes disappointing results’. However, economic tools, when properly used, lead to useful results and provide helpful recommendations for decision-makers and funders. Examples of this include the work of Lacny et al. (2016) on a pre-study, which comes to the conclusion that a nursing intervention combined with medical intervention is superior to medical intervention alone in a Canadian health care home using an incremental cost-effectiveness ratio calculation. Similarly, an article by Mourgues et al. (2018) compared a follow-up programme for the comorbidities of arthritis patients to more conventional intervention. The purpose of this article was to determine at what level of intervention the use of nursing intervention is cost-effective. The cost of the intervention was assessed at €16,804.2. This intervention contributed to the performance of 747 additional preventive procedures, at a cost of €30,184.8. This intervention with these patients was financially balanced when at least 37 patients followed the recommendations for each preventive procedure. Ultimately, we wish to encourage nursing researchers to think about cost-effective medical tools as an aid in evaluating nursing interventions. None. No conflict of interest has been declared by the authors. Charline Mourgues: Conception and design. Charline Mourgues and Alexandra Usclade: Analysis and interpretation. Charline Mourgues: Writing. Charline Mourgues and Alexandra Usclade: Writing – review and editing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.219
metaresearch head score (Gemma)0.520
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.219
Threshold uncertainty score0.962

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2190.520
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0120.008
Bibliometrics0.0150.019
Science and technology studies0.0010.002
Scholarly communication0.0120.008
Open science0.0030.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.209
GPT teacher head0.503
Teacher spread0.294 · 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.

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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Citations0
Published2022
Admission routes1
Has abstractyes

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