Managing hidden illnesses that impact on performance and absenteeism
Bibliographic record
Abstract
Under-performance and absenteeism are issues that all organisations seek to reduce, often devoting substantial resources to the establishment of performance management policies, extensive training programs and a host of complementary policies. However, while most contributory factors to these unwelcome workplace issues are known and tackled, one factor stands alone as a key contributor to sub-optimal performance and poor attendance, namely mental illness. This paper oultines the extent of mental illness in western countries, why it is hidden, and how the use of additional policies can be adopted to assist employees who choose not to divulge to their employer that they have a mental illness. Under-performance and absenteeism are issues that all organisations seek to reduce, often devoting substantial resources to the establishment of performance management policies, extensive training programs and a host of complementary policies. However, while most contributory factors to these unwelcome workplace issues are known and tackled, one factor stands alone as a key contributor to sub-optimal performance and poor attendance, namely mental illness. This factor, while acknowledged is seriously under-valued in size and breadth of coverage in the workplace with few operational managers knowing that in any calendar year somewhere between a fifth and a quarter of their staff will have a mental illness. The effect of mental illness on fitness to work is not well known in management literature as its incidence is shrouded in secrecy and subterfuge by its sufferers, most of whom seek to attribute changes to performance and attendance to other factors. Consequently, performance management plans will rarely have this information available, even when employees are directly asked if they have a health problem affecting their fitness for work. The pervasive nature of stigma that surrounds mental illness keeps it hidden and away from public view and the wider community, few people knowing that in any 12-month period mental illness accounts for 20-27% of any community (NIMHa, 2011; ABS, 2007; Wittchen and Jacobi, 2005). Fears of a negative backlash after disclosing such illness is so powerful, that employees with temporary or long-term mental illness would rather attribute performance issues to other problems rather than to run the risk of being viewed with the prevailing stereotype that surrounds sufferers of these types of illnesses. Hence, when performance issues arise, such employees are often resistant to the agreed goals of performance plans, often to the frustration of operational managers and human resource staff alike. This paper will outline the incidence of mental illnesses in the workplace, showing why it is concealed and common effects of mental illness on employee performance and attendance. Strategies to effectively manage employees with a hidden mental illness will be reviewed and outlined, Currently both management and human resource literature fail to provide adequately for the management of employees with a hidden mental illness.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".