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Record W4224311552 · doi:10.2196/35363

Nurses’ Perspectives on an Electronic Medication Administration Record in Home Health Care: Qualitative Interview Study

2022· article· en· W4224311552 on OpenAlexvenueno aff
Sara Karnehed, Lena‐Karin Erlandsson, Margaretha Norell Pejner

Bibliographic record

VenueJMIR Nursing · 2022
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
Fundersnot available
KeywordseHealthNursingFocus groupQualitative researchGrounded theoryHealth careTransparency (behavior)MedicineQualitative propertyPsychologyBusinessSociology

Abstract

fetched live from OpenAlex

BACKGROUND: eHealth is considered by policy makers as a prerequisite for meeting the demands of health care from the growing proportion of older people worldwide. The expectation about what the efficiency of eHealth can bring is particularly high in the municipal home health care sector, which is facing pressure regarding resources because of, for example, earlier discharges from hospitals and a growing number of patients receiving medications and treatments at home. Common eHealth services in home health care are electronic medication administration records (eMARs) that aim to communicate delegated tasks between professionals. However, there is an extensive gap in the research on how technology affects and is experienced by home health care professionals. OBJECTIVE: The objective of this paper is to shed light on how home care nurses experience eMARs in a Swedish municipality. METHODS: This qualitative interview study was conducted among home health care nurses using eMARs to facilitate communication and signing of delegated nursing tasks. The analysis of the interviews was performed using constructivist grounded theory, according to Charmaz. RESULTS: Of the 19 day-employed nurses in the municipality where an eMAR was used, 16 (84%) nurses participated in the study. The following two categories were identified from the focus group interviews: nurses become monitors and slip away from the point of care. The nurses experienced that they became monitors of health care through the increased transparency provided by the eMAR and the measurands they also applied, focusing on the quantitative aspects of the delegated nursing tasks rather than the qualitative aspects. The nurses experienced that their monitoring changed the power relations between the professions, reinforcing the nurses' superior position. The experience of the eMAR was regarded as transitioning the nurses' professional role-away from the point of care and toward more administration-and further strengthened the way of managing work through delegation to health care assistants. CONCLUSIONS: Previous analyses of eHealth services in health care showed that implementation is a complex process that changes health care organizations and the work of health care professionals in both intended and unintended ways. This study adds to the literature by examining how users of a specific eHealth service experience its impacts on their daily work. The results indicate that the inscribed functions in an eHealth service may affect the values and priorities where the service is in use. This presents an opportunity for future research and for health care organizations to assess the impacts of specific eHealth services on health care professionals' work and to further examine the effects of inscribed functions in relation to how they may affect actions and priorities at individual and organizational levels.

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)0.019
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.007
Scholarly communication0.0050.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.555
Teacher spread0.474 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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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Citations11
Published2022
Admission routes1
Has abstractyes

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