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Record W2526223753 · doi:10.5539/gjhs.v9n5p85

Threats and Opportunities of the Health Reform Plan from the Nurses’ Perspective in Ilam

2016· article· en· W2526223753 on OpenAlexvenueno aff
Masoumeh Shohani, Firoz Balavandi, Řeža Valizadeh, Hamed Tavan

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsStrengths and weaknessesPlan (archaeology)Descriptive statisticsData collectionMedicinePerspective (graphical)PsychologyMedical educationGeographyStatisticsSocial psychologyMathematics

Abstract

fetched live from OpenAlex

<p><strong>BACKGROUND</strong><strong>: </strong>The Health Reform Plan is one of the greatest state services in Iran. However, this plan has its own weaknesses and strengths. This study was conducted with the purpose of determining strengths and weaknesses of the Health Reform Plan from nurses' perspective.</p><p><strong>METHODS: </strong>This is a cross-sectional study in which 100 nurses who work in clinical education centers on Ilam participated. The data collection tool was a questionnaire which consisted of 12 items regarding the strengths of the Health Reform Plan and 18 items about the plan's weaknesses on a six-option Lickert scale. Data analysis was carried out using SPSS V. 19 and applying descriptive and inferential statistics.</p><p><strong>RESULTS:</strong> The mean score for weaknesses was 79.94 and the mean score for strengths was 52.49. There was a significant statistical relationship between the variable of age and the strengths (P=0.015).</p><p><strong>CONCLUSION: </strong>If we manage to increase strengths and reduce weakness of the Health Reform Movement, we can be hopeful that this great plan will be administered more efficiently at a national level. It is suggested that future studies be conducted about individuals' perspectives in other occupations in the field of medical sciences working in different medical communities and hospitals in Iran and the results be compared.</p>

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.224
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.134
GPT teacher head0.477
Teacher spread0.343 · 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.

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

Citations1
Published2016
Admission routes1
Has abstractyes

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