Leadership development in health information management (HIM): literature review
Bibliographic record
Abstract
PURPOSE: The health information management (HIM) profession lacks clarity around leadership and leadership development. To date, little empirical research exists on this topic, and it is unclear if broader approaches for healthcare leadership are suitable. This paper aims to explore which the leadership styles are relevant to the HIM profession. The findings were also used to inform a discussion on how HIM professionals could develop these leadership styles. DESIGN/METHODOLOGY/APPROACH: Through a systematic scoping literature review, deductive thematic analysis was undertaken to extrapolate common themes around this style of leadership based on transversal competency domains that reflect twenty-first century skills (i.e. critical thinking and innovation, interpersonal, intrapersonal and global citizenship) (Bernard, Watch and Ryan, 2016; UNESCO, 2015 ). This approach enabled the findings to be discussed from a leadership development perspective. FINDINGS: Analysis of the literature revealed that a relational leadership style through a team-based approach is required. Literature studies on how to develop leadership competencies were not found. RESEARCH LIMITATIONS/IMPLICATIONS: Future policy and research implications include the need for research on transversal competencies to determine if they can shape HIM leadership development. PRACTICAL IMPLICATIONS: This leadership style and competencies proposed are relevant across many occupations and may have broader applications for leadership research, education and development. ORIGINALITY/VALUE: This paper defines the style of leadership required in the HIM profession and identifies a succinct set of contemporary competencies to inform the development of this type of leadership.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".