Compliance with Continuing Professional Development (IES7) of Internal Auditor and Quality of Internal Audit Function
Bibliographic record
Abstract
This paper aims to highlight the importance of compliance with continuing professional development (CPD) of internal auditors and its relationship to the quality of internal audit. The study utilised a Partial Least Squares Structural Equation Modelling (PLS-SEM), which emphasise on the analyses of regression and variance by distributing a questionnaire to the study sample of internal auditors in four countries (Jordan, Lebanon, Qatar and Kuwait), and the number of samples valid for statistical analysis reached 104. The paper has reached positive results regarding the importance of CPD in improving the quality of the internal audit, focused on the continuous follow-up to changes related to income and sales tax law, international financial reporting standards (IFRS), International Accounting Standards (IAS), and International Auditing Standards (IAS). Besides, modern Computer and Technological Applications in Internal audit practices and audit ethics. The results also indicated that CPD represents in professional skills is the development of skills to detect and combat theft and fraud, critical thinking skills of analyzed data. Original/Value, the CPD is the impulse by which internal auditors continue to learn new technical knowledge and skills that support them in professional work, to make them more qualified and developed in terms of knowledge and the skills needed within the labour markets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".