Evidence map›Paper›PMID 28339684›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2017

Embedding Nursing Interventions into the World Health Organization's International Classification of Health Interventions (ICHI).

Nicola Fortune, Nicholas R Hardiker, Gillian Strudwick

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Nicola FortuneNational Centre for Classification in Health, Faculty of Health Sciences, University of Sydney, Lidcombe, Australia.
Nicholas R HardikerSchool of Nursing, Midwifery, Social Work and Social Sciences, University of Salford, Salford, UK.
Gillian StrudwickLawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe International Classification of Health Interventions, currently being developed, seeks to span all sectors of the health system. Our objective was to test the draft classification's coverage of interventions commonly delivered by nurses, and propose changes to improve the utility and reliability of the classification for aggregating and analyzing data on nursing interventions. MATERIALS AND

methodsA 2-phase content mapping method was used: (1) three coders independently applied the classification to a dataset comprising 100 high-frequency nursing interventions; (2) the coders reached consensus for each intervention and identified reasons for initial discrepancies.

resultsA consensus code was found for 80 of the 100 source terms; for 34% of these, the code was semantically equivalent to the source term, and for 64% it was broader. Issues that contributed to discrepancies in Phase 1 coding results included concepts in source terms not captured by the classification, ambiguities in source terms, and uncertainty of semantic matching between "action" concepts in source terms and classification codes. DISCUSSION: While the classification generally provides good coverage of nursing interventions, there remain a number of content gaps and granularity issues. Further development of definitions and coding guidance is needed to ensure consistency of application.

conclusionThis study has produced a set of proposals concerning changes needed to improve the classification. The novel method described here will inform future health terminology and classification content coverage studies.

Indexed as

Nursing InformaticsVocabulary, ControlledHumansNursing ProcessWorld Health Organizationclassificationdata aggregationnursing informaticsterminologyWorld Health Organization

Identifiers

PMID28339684
PMCPMC7651898

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.