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Contributing Factors to Job Preparedness among Undergraduate Students: A Rapid Review

2024· review· en· W4403282114 on OpenAlexaboutno aff
Teoh Sian Hoon

Bibliographic record

VenuePakistan Journal of Life and Social Sciences (PJLSS) · 2024
Typereview
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersUniversiti Teknologi MARA
KeywordsPreparednessMedical educationPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Undergraduate students, who are responsible for equipping themselves with the university's guidance, possess a mutual vision in getting well for job preparedness.However, the endeavours are not promising for obtaining employment.This study seeks to develop a solid body of information regarding the elements that contribute to work preparation among undergraduate students through a comprehensive and efficient evaluation of existing literature.A rapid literature review method was employed in selecting the ten relevant articles.The process involved searching articles from two databases using specific keywords.The screening process involved the exclusion criteria, which encompassed studies that focused solely on evaluating the effectiveness of a particular job-related training and samples that were not undergraduate students.The results revealed that the papers were authored by researchers from the United States, China, Australia, Canada, and the United Kingdom.The relevant factors discovered were categorised as either environmental, personal, or a combination of both.However, personal factors have received greater emphasis.The findings revealed that it is crucial to encourage individuals' ideas and motives, which are essential for ensuring mental well-being.In addition, the environmental aspects were portrayed as resources, providing opportunities for students to adequately prepare for their profession.However, the degree to which students are job-prepared heavily relies on their awareness of the knowledge they have acquired, enabling them to be self-assured and accountable to both themselves and society.Therefore, it is recommended that universities provide additional programmes that not only prioritise education but also aim to raise awareness of their capabilities.

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)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0130.011
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.001
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 head0.108
GPT teacher head0.490
Teacher spread0.382 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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