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Nursing Care and Patient Safety: Visualizing Medication Organization, Storage and Distribution with Photographic Research Methods

2010· article· en· W1998743729 on OpenAlexafffund
Anna Carolina Raduenz, Priscila Hoffmann, Vera Radünz, Grace Teresinha Marcon Dal Sasso, Isabel Cristina Alves Maliska, Patrícia Marck

Bibliographic record

VenueRevista Latino-Americana de Enfermagem · 2010
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsUnit (ring theory)NursingQualitative researchPatient safetyMedicineHealth carePsychologySociology

Abstract

fetched live from OpenAlex

In this qualitative study, we adapted photographic research methods from earlier nursing research to identify factors related to organization, storage and distribution that could lead to errors in the selection, preparation and administration of medications. The research excerpt presented here was developed in a clinical unit of an urban Brazilian public hospital. The research participants were nurses working at that unit and students from the two final semesters of the Undergraduate Nursing Course. We collected digital photographs of the medication system and subsequently used photo elicitation to review the images with research participants, so as to obtain their perceptions and narratives of working with medications in the unit. We report selected findings here on the organization, storage and distribution of medications, which indicate there is room to improve the safety of the medication system.

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)0.014
metaresearch head score (Gemma)0.020
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 score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0030.006
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.413
Teacher spread0.387 · 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

Citations25
Published2010
Admission routes2
Has abstractyes

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