Skills and abilities to thrive in remote work: What have we learned
Bibliographic record
Abstract
The COVID-19 pandemic led to a rapid acceleration in the number of individuals engaging in remote work. This presented an opportunity to study individuals that were not voluntarily working remotely pre-pandemic and examine how they adapted and learned to achieve success in a remote work environment, at an organization that did not have substantial prior experience managing remote work. We used a semi-structured interview process to interview participants (n = 59) who occupied both Individual Contributor and Leadership levels at an organization and broadly representative across several important demographic characteristics. We asked participants to discuss what factors at individual, team, and organizational levels contributed positively toward their remote work experience, which factors presented challenges to remote work, and what could be done to ensure success with remote work in the future. Interviews were analyzed utilizing a thematic analysis approach and summarized into common themes pertaining to factors that influence success in a remote working environment. Themes were used to identify specific skillsets particularly relevant to remote work that would benefit from training, as well as important organizational culture changes and policies needed to support remote workers and ensure their success. We present these and other findings in relation to current research and provide recommendations for 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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".