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Enregistrement W4283327466 · doi:10.1111/jan.15272

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

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

Notice bibliographique

RevueJournal of Advanced Nursing · 2022
Typeletter
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychological interventionMedicineNursing Interventions ClassificationRandomized controlled trialNursingMeta-analysisIntervention (counseling)Cost–benefit analysisMEDLINEEconomic evaluationFamily medicineIntensive care medicineSurgery

Résumé

récupéré en direct d'OpenAlex

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.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,219
score de la tête « metaresearch » (Gemma)0,520
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,219
Score d'incertitude au seuil0,962

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,2190,520
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0120,008
Bibliométrie0,0150,019
Études des sciences et des technologies0,0010,002
Communication savante0,0120,008
Science ouverte0,0030,004
Intégrité de la recherche0,0030,005
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,209
Tête enseignante GPT0,503
Écart entre enseignants0,294 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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