Internal and external validity of Chen et al.'s nursing‐sensitive quality indicators for the neonatal intensive care unit
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.203 | 0.391 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".