Development and field testing of a multidimensional tool for benchmarking knowledge worker productivity
Bibliographic record
Abstract
The impact of indoor environmental quality on human health and wellbeing has been widely documented, as has its impact on worker productivity in settings such as education, healthcare, and manufacturing. In the knowledge worker context, however, there is limited consensus on agreed metrics for productivity measurement, leading to a paucity of research regarding the impact of specific realworkplace interventions on worker performance. This paper presents an organizational benchmarking and evaluation tool to permit such tracking within an organization. Based on the results of a systematic literature review, five dimensions are used to evaluate productivity in this context: absenteeism, presenteeism, engagement, self-assessed (individual) productivity, and objective (office-wide) productivity. This data is collected from organizational reports, individual employee questionnaires, and field data. The resultant tool was refined through a series of public- and private-sector field tests and the evaluation of results was undertaken through post-analysis interviews with organizational partners who provided feedback on the clarity of data as-presented, alignment with known issues, and consistency with other studies. The evaluated tool provides a holistic means of assessing knowledge worker productivity and will support future research to evaluate and quantify the impact of specific interventions in workplace policy and environmental modifications.
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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 | 0.078 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".