The Effects of Lighting on Mood in the Workplace: A Literature Review of the Research Method Applied
Bibliographic record
Abstract
Studies on the effect of lighting on mood have used different information-gathering methods and have presented a range of different conclusions.This paper aims to review the previous research relating to the applied method.This review method was conducted by selecting relevant articles through skimming and scanning, informed by the PRISMA protocol.The literature review discussed the subject, object, purpose, and applied research treatment, as well as the data collection methods of the selected papers.The result was 1) the subjects' terms and conditions that are required to be a research participant, 2) the object's description of the previous studies' test cell, 3) the treatment applied to achieve research purposes that focused on: a) illuminance levels, b) correlated colour temperature (CCT), or c) a combination of both, and 4) the data collection that listed information collected and the data/information gathering method for effective implementation.This literature review discussion could be beneficial for constructing a research plan about the effect of lighting on an individual's mood.However, further research might still be needed to ensure that the research design regarding the chosen subject, object, applied treatment, and data collection method can support achieving the research purpose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".