Relationship Between Leadership, Resilience, and Competence Amongst Police Officers in Klang Valley, Malaysia
Bibliographic record
Abstract
Excellent human resource development prioritizes organizational performance development elements. Organizational performance in Malaysia’s public sector is a concept that still needs to be explored. To date, improvements to leadership quality in order to enhance employee competence is one of the areas of study that has become the focus of researchers in the field of human resource development. In fact, leadership quality is also influenced by a person’s self-resilience to changes – one such example is police officers’ competence in order to perform their duties well. This study aims to assess the relationship between self-resilience and the leadership qualities of police officers. The study involved the Royal Malaysia Police of the state of Selangor. The study which used a simple randomized quantitative method involved 105 respondents comprised of police officers and other members of the force. Findings of the study indicate highest positive relationships between leadership and competency, resilience and competency, and resilience and leadership, with r values between 0.791 to 0.864. However, the relationship between leadership quality based on education level and length of service (work experience) was not significant. This study shows that there are several elements in human resource development and performance management that can be improved by emphasizing on the leadership aspect in order to improve the competencies of police officers in Malaysia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".