MétaCan
Menu
Back to cohort
Record W2112107001 · doi:10.1177/1049732314524487

Confirming Delivery

2014· article· en· W2112107001 on OpenAlexaff
Marilyn Macdonald, MarySue V. Heilemann, Neil J. MacKinnon, Ariella Lang, David Gregory, Mary Ellen Gurnham, Theresa Fillatre

Bibliographic record

VenueQualitative Health Research · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsNova Scotia Health AuthorityCapital District Health AuthorityUniversity of ReginaCanadian Patient Safety InstituteVictorian Order of NursesDalhousie University
Fundersnot available
KeywordsQualitative researchNursingGrounded theoryMedicinePatient safetyAdministration (probate law)InterdependenceConstructivist grounded theoryPsychologyHealth care

Abstract

fetched live from OpenAlex

The purpose of our study was to gain an understanding of current patient involvement in medication administration safety from the perspectives of both patients and nursing staff members. Administering medication is taken for granted and therefore suited to the development of theory to enhance its understanding. We conducted a constructivist, grounded theory study involving 24 patients and 26 nursing staff members and found that patients had the role of confirming delivery in the administration of medication. Confirming delivery was characterized by three interdependent subprocesses: engaging in the medication administration process, being "half out of it" (patient mental status), and perceiving time. We believe that ours is one of the first qualitative studies on the role of hospitalized patients in administering medication. Medication administration and nursing care systems, as well as patient mental status, impose limitations on patient involvement in safe medication administration.

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)0.012
metaresearch head score (Gemma)0.059
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.059
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.004
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0310.009

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 head0.810
GPT teacher head0.740
Teacher spread0.070 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueQualitative Health ResearchSame topicPatient Safety and Medication ErrorsFrench-language works237,207