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Record W2975746710 · doi:10.21694/2379-2922.17002

Workarounds to Medication Preparation and Administration within an Intensive Care Unit: A Qualitative Study

2017· article· en· W2975746710 on OpenAlexafffund
Fernanda Re Gimenes, Patrícia Marck, Elisabeth Atila, Mayara Carvalho, Godinho Rigobello, Ana Gobbo Motta, Emanuel Nunes, R. M. S. C. Pereira, Fernanda Raphael Escobar Gimenes, Mayara Godinho

Bibliographic record

VenueAmerican Research Journal of Nursing · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Victoria
FundersGovernment of Canada
KeywordsWorkaroundIntensive care unitAdministration (probate law)Qualitative researchMedicineUnit (ring theory)Medical emergencyPsychologyIntensive care medicineComputer scienceSociologyPolitical science

Abstract

fetched live from OpenAlex

Objectives: To gain a comprehensive understanding of medication safety and potential improvements in a Brazilian intensive care unit (ICU), we included an exploration of related workarounds. Methods:We adapted participatory photographic research methods from the field of ecological restoration to study a Brazilian ICU.Using focus groups, nurse-led photo-narrated walkabouts, and photo elicitation in iterative phases of data collection and analysis, we identified a theme of 'living with workarounds on a day-today basis'.Results: Participants recognized barriers within their work environment that might contribute to perpetuating medication workarounds, and the visual methods enabled them identify ideas to minimize workarounds related to these processes of care. Conclusions:The participatory photo methods helped participants and researchers to develop in depth discussions to understand how nurses work around systemic vulnerabilities to optimize the delivery of patient care.The methods also increased participants' awareness of such behaviors and provided opportunities to identify ideas to reduce risk.We expect that similar methods could be successfully used in the future in a variety of practice settings to improve medication processes and other patient safety issues.

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)0.015
metaresearch head score (Gemma)0.033
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 score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.009
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0020.002
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 head0.477
GPT teacher head0.689
Teacher spread0.212 · 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".

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

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