Risk and Opportunity—The Leadership Challenge in a World of Uncertainty—Learnings from Research into the Implementation of the Australian National Disability Insurance Scheme
Bibliographic record
Abstract
This paper considers the risks and opportunities inherent in a major national change process through a descriptive approach to the implementation challenges for Australian non-profit disability service providers as they grapple with the implementation of the transformational National Disability Insurance Scheme (NDIS). It highlights the leadership challenges associated with the newly developed NDIS Implementation Framework and, in doing so, recognises the risk and opportunity issues contained with that implementation process. The research used grounded theory coupled with framework analysis in a qualitative study that, in part, sought to identify leadership characteristics deemed necessary to minimize risks, capitalize on opportunities, and support positive change outcomes leading to successful NDIS implementations amongst several participating organisations, each with differing demographics and at different stages in the implementation process. The findings, which have been grouped into phases, suggest a range of leadership attributes at key phases of the NDIS implementation that are necessary to minimise implementation risks and maximise opportunities associated with the NDIS. These phases have been identified as: (i) An input phase where the emphasis must be on internal change preparedness and external environmental impacts and drivers; (ii) A process phase where the emphasis is on direct implementation issues; and (iii) An outcomes phase where active consideration needs to be on organisational mission sustainability, as well as the risk and opportunity challenge. The study is crucial in revealing leadership challenges and lessons for large scale change and risk management in the non-profit sector, within and beyond the specific case of Australia’s NDIS implementation, useful for both scholars and practitioners.
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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 | 0.050 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| 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".