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Record W4387256516 · doi:10.35516/hum.v50i4.5642

Voice and Agency: Evaluating the “Empowering Women for Leadership in Administration Roles” Training Program Held at Yarmouk University

2023· article· en· W4387256516 on OpenAlexaboutno aff
Amneh Khasawneh, Ruba Al Akash, Amneh Al-Rawashdeh, Nowar Al Hamad, Tamara Al-Yakoub, Heyam Al Khatib

Bibliographic record

VenueDirasat Human and Social Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
FundersYarmouk University
KeywordsAgency (philosophy)Medical educationPsychologyLeadership developmentPsychological resilienceTraining (meteorology)Administration (probate law)EmpowermentQualitative researchMedicinePublic relationsPolitical scienceSociologySocial psychology

Abstract

fetched live from OpenAlex

Objectives: The objective of this qualitative research is to evaluate the effectiveness of a training program directed towards the female administrative staff at Yarmouk University (YU), called "Empowering Women for Leadership Roles". The program ran from 2018 to 2020 at YU and was funded by Global Affairs Canada. Sixty female participants successfully completed ten training modules within the program to enhance their personal and professional capacity. Methods: The researchers conducted semi-structured interviews with 15 participants, and the interview questions were based on Kirkpatrick’s training evaluation model. Results: The main results revealed that the training program facilitated the women’s learning, enriched their skills, and helped them cultivate resilience, enabling them to draw upon their personal strengths to overcome adversity and marginalization. Conclusions: Based on the results, it is concluded that women should receive training in the early stages of their careers, enabling them to deploy their acquired skills to promote career advancement. Additionally, women holding mid and senior positions must receive continuous support through training and development programs to enhance their leadership skills.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.645
GPT teacher head0.447
Teacher spread0.198 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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