MétaCan
Menu
Back to cohort
Record W603624013

Оценка эффективности организаторской деятельности медицинских сестер

2013· article· ru· W603624013 on OpenAlexaboutno aff
А К Тургамбаева, С. В. Сарсенова, Д Б Кулов, С. Ж. Ильясов, Д. М. Имашпаев

Bibliographic record

VenueКлиническая медицина Казахстана · 2013
Typearticle
Languageru
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)NursingJob satisfactionSubsidiaryPrincipal (computer security)PsychologySettlement (finance)Medical educationMedicineBusinessGeographySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Im: This study investigates the views of the principal and senior nurses (nurse managers) subsidiaries of JSC “National Medical Holding” about various aspects of their professional activities. Materials and methods: A study of nurses concerned the following characteristics: age, professional work experience, education, qualification category, job satisfaction. The study was conducted among 131 nurses who are in managerial positions. results: The mean age was in the range 35-39 years, most of the staff has been employed for more han 10 years. More than half of those surveyed have a secondary medical education, and the quarter the highest nursing. Highest qualification awarded almost half of the respondents. Unfortunately, the degree of job satisfaction, wages and growth prospects in most cases was the average. The analysis showed that at the present time in Kazakhstan problem with the activities of nursing staff is solved still dormant. Management system is not perfect and the settlement of paramedical workers.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.005

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 head0.044
GPT teacher head0.285
Teacher spread0.241 · 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 designObservational
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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueКлиническая медицина КазахстанаSame topicMedical and Biological SciencesFrench-language works237,207