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Enregistrement W2735733712 · doi:10.1111/jocn.13956

Internal and external validity of Chen et al.'s nursing‐sensitive quality indicators for the neonatal intensive care unit

2017· article· en· W2735733712 sur OpenAlexaff
Catherine Hupé, Andréane Lavallée

Notice bibliographique

RevueJournal of Clinical Nursing · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Maternal and Child Health
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésDelphi methodBreastfeedingMedicineNursingAudience measurementHealth careIntensive careQuality (philosophy)Neonatal intensive care unitPediatricsFamily medicinePolitical scienceLaw

Résumé

récupéré en direct d'OpenAlex

As clinical nurse specialists in neonatology and quality of care evaluation, we read with great interest Lin Chen and her colleagues' article (Chen et al., 2017) about the development of nursing-sensitive quality indicators using a Delphi method. However, in our opinion, the authors' methodological choices generate serious questions regarding the validity of these indicators. Indeed, according to Mainz (2003), an ideal indicator has to be relevant for clinical practice, based on an agreed definition, based on scientific evidence and valid. First of all, we are questioning the author's decision to initially exclude from the study some indicators highly relevant to clinical practice in neonatal intensive care units without clear justification or because of their inconsistency with current Chinese policies (Chen et al., 2017). Breastfeeding rates and family satisfaction are among the excluded indicators. For that matter, it would have been wise to add “in China” to the title so that the international readership of the JCN take caution when considering the generalisability. It also appears that a nurse-sensitive indicator like breastfeeding or human milk feeding rate is too clinically important to be ignored. We are talking about a global public health priority that is considered as the gold standard worldwide for its benefits on health and mortality, particularly in low-birthweight infant (American Academy of Pediatrics, 2012; Canadia Paediatric Society, 2012; Paediatric Nursing Associations Of Europe, 2009; World Health Organization, 2016; World Health Organization and UNICEF, 2009). Moreover, the eleven proposed indicators (Chen et al., 2017) have not been defined, which leads to confusion as to what is actually being measured. For example, the authors mention the “compliance of handwashing techniques.” Without a definition, the readership could expect a process indicator, that is an assessment of the participant's handwashing technique (each step properly performed), whereas the frequency of handwashing and the number of hand sanitisers requisitioned are suggested. In addition, consensus techniques like Delphi are based on a rigorous and structured review of literature, which constitutes a fundamental step prior to the development of indicators (Campbell, Braspenning, Hutchinson, & Marshall, 2002). Readers interested in the credibility of the indicators should be able to consult the outline of the literature review: objectives, eligibility criteria, characteristics of the publications, etc. However, the only information provided in the article (Chen et al., 2017) is a search strategy based on systematic reviews and meta-analysis indexed in a few general health databases. It should be noted that the most frequently used methods for the development of nursing indicators are the focus group, the Delphi technique and the survey (Xiao, Widger, Tourangeau, & Berta, 2017), which are nonexperimental methods usually excluded from systematic reviews and meta-analyses. As for the content validity of the indicators, Chen et al. (2017) mention a W-value coefficient of concordance of the two rounds ranging from 0.212 to 0.446. They qualify this coefficient as being an excellent agreement between the expert panellists, without supporting this interpretation by a reference. According to Schmidt (1997), a result of <0.50 corresponds to a level of agreement that is qualified as very low (0.10–0.30) or low (0.30–0.49). In sum, we believe managers and nurses working in neonatal clinical settings should, in the first place, clearly define the indicators relevant to their clinical and cultural context, which should include breastfeeding rates, and then evaluate the appropriateness of the formulas (denominators and numerators) suggested in Chen et al.'s paper (2017). A complimentary review of nursing-sensitive quality indicators in neonatal intensive care unit based on a rigorous method used in recognised nursing databases such as CINAHL and MEDLINE is needed and therefore recommended.

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)0,203
score de la tête « metaresearch » (Gemma)0,391
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,203
Score d'incertitude au seuil0,983

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,2030,391
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,004
Bibliométrie0,0050,005
Études des sciences et des technologies0,0020,005
Communication savante0,0040,003
Science ouverte0,0020,006
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,131
Tête enseignante GPT0,517
Écart entre enseignants0,385 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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
Publié2017
Routes d'admission1
Résumé présentoui

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