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Record W4385169323 · doi:10.53555/sfs.v10i1.1378

Effect Of Work Environment Of Asha Workers On Their Performance In Thiruvananthapuram

2023· article· en· W4385169323 on OpenAlexvenueno aff
Chandra Prakash

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicLeadership, Behavior, and Decision-Making Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAshaSimple random sampleAffect (linguistics)Work (physics)ProductivitySample (material)PsychologyData collectionApplied psychologyEnvironmental healthMedicineEngineeringStatisticsMathematicsEconomic growth

Abstract

fetched live from OpenAlex

The environment in which a person works has got the potential to affect both his personal life as well as professional life. The work environment supported by the superiors and the co-employees pave the way to better productivity and performance of that person. The same is applicable in the case of the ASHA workers. In this view, the current study is an attempt to explore the impact of work environment over the performance of the ASHA workers. The respondents are the ASHA workers who have been selected through the simple random method of sampling. The sample size of the study is 100. The tool used for data collection is a questionnaire and the instrument used for analysis of the collected data is Regression analysis. The outcomes of the analysis show that there is significant relationship between work environment and Work performance of ASHA 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.357
GPT teacher head0.378
Teacher spread0.020 · 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 teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

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