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Record W2143617057 · doi:10.5430/jha.v4n5p90

A research about job satisfaction of medical secretaries working in a public hospital in the city center of Diyarbakir, Turkey

2015· article· en· W2143617057 on OpenAlexvenueno aff
Rojan Gümüş, Arif Şahin

Bibliographic record

VenueJournal of Hospital Administration · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaPromotion (chess)Job satisfactionMarital statusAffect (linguistics)PsychologyPopulationGovernment (linguistics)WorkforceScale (ratio)Family medicineDescriptive statisticsTest (biology)MedicineNursingSocial psychologyClinical psychologyGeographyEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Objective: The aim of this study was to investigate the level of job satisfaction among medical secretaries and to identify the major predictors that affect their complaints about work.Methods: The data were obtained from 115 medical secretaries working in a public hospital. We didn’t use any sampling method but tried to reach the whole population in the hospital. As a survey method, Spector Job Satisfaction Survey (JSS, 1985) has been used. JSS is a 36 item, nine facet scale (pay, promotion, supervision, fringe benefits, contingent rewards, operating conditions, coworkers, nature of work and communication) in order to assess employee attitudes about the job and aspects of the job. Its reliability has been measured by the Cronbach Alpha test. Employees were compared according to their gender, age, marital status, education, working status (official-employed by government/ casual staff-employed by firms), working experience (years) and working position (administrative-clinical). Descriptive statistics, significance tests and confidence tests were gained by SPSS 16.0.Results: Findings showed that gender and marital status affect satisfaction but age doesn’t play a big role. Male participants were more satisfied in terms of promotion (χ2 = 27.506, p < .001) and supervision (X2= 29.852, p < .001) facets. In terms of pay (X2 = 15.764, p < .01), fringe benefits (X2 = 24.948, p < .001), and contingent rewards (X2 = 26.396, p < .001) single participants were more satisfied relative to married ones. Also, participants who had working experience over 10 years were more satisfied. The highest significance levels were seen in working position (administrative/clinical) and working status (official-casual). Participants working official and at administrative positions were more satisfied on various facets.Conclusions: Based on outcomes of this study, it is observed that lowest satisfaction levels were found in pay, promotion, and communication facets. Participants were more satisfied with nature of work, supervision and fringe benefits. Participants working at administrative positions were more satisfied in terms of supervision, coworkers, and nature of work facets. In clinics health workers have more work to do and this may lead to dissatisfaction in relationships with coworkers and supervisors. Because of the fact that working casually means inconsistency of work, hard working conditions, lower payments and impossibility of promotion, participants working casually were dissatisfied in terms of pay, supervision, coworkers, communication and nature of work facets. It is obviously seen that the government has deficiency about describing responsibilities and rights of medical secretaries. The status of work should be strengthened and their future at work should be taken under assurance.

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)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.102
GPT teacher head0.396
Teacher spread0.294 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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