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Record W2071161564 · doi:10.5430/jha.v4n3p45

Effect of reward system among health care workers performance: a case study of university of Calabar teaching hospital Calabar, Nigeria

2015· article· en· W2071161564 on OpenAlexvenueno aff
Oyira Emilia James, Regina Etita Ella, Nkamare S.E, Felicia Ekwok Lukpata, Sylvia Lazarus Uwa, Partric Awok Mbum

Bibliographic record

VenueJournal of Hospital Administration · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsDeskReward systemHealth carePsychologyMedical educationNursingMedicineEngineeringEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The study investigated the effect of reward system on health care workers performance in Teaching Hospital. It examined the relationship among monetary and non-monetary rewards and employees’ performance in University of Calabar Teaching Hospital (UCTH). Desk survey was used in gathering relevant information. Primary sources were questionnaire, observation and interview, while secondary data were gathered from internet, textbooks, journals and libraries. Chi-square statistical tool was used and the findings revealed the monetary reward had a positive impact on employees’ performance while non-monetary rewards had a negative effect on employees’ performance. The study recommended that management of UCTH should boost the morale of their employees through fair and equitable reward system. The study further recommended that management should be effective with monetary rewards like bonuses and fringe benefits to encourage the workers improve performance.

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)0.003
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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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
Published2015
Admission routes1
Has abstractyes

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