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Record W4294792078 · doi:10.52113/6/2022-12-1/38-51

The impact of training quality on developing human resources capabilities / an applied study at the National Center for Training and Human Development in the Ministry of Health

2022· article· en· W4294792078 on OpenAlexaff

Bibliographic record

VenueMuthanna Journal of Administrative and Economics Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsImpact
Fundersnot available
KeywordsChristian ministryTraining (meteorology)Human resourcesCenter (category theory)Quality (philosophy)Training and developmentHuman healthMedical educationBusinessMedicinePolitical scienceEnvironmental healthManagementGeography

Abstract

fetched live from OpenAlex

This study dealt with the impact of the quality of training in helping individuals and professional institutions in building and developing human capabilities, as a theoretical framework was presented for some of what the writers and researchers put forward on the subject of training quality and its role in developing the capabilities of workers in various jobs, in addition to the practical framework, which included a review of some of the results of the training aspects in The National Center for Training and Human Development in the Ministry of Health, or the research included four main axes: A- The goal of the research. BThe procedures followed in the practical aspect C- The most important conclusions D- The most important recommendations

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)0.030
metaresearch head score (Gemma)0.046
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0000.001
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 head0.443
GPT teacher head0.496
Teacher spread0.053 · 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
Published2022
Admission routes1
Has abstractyes

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