Technostress or reaction to techno-stressors? Validation of bilingual techno-stressors index (TSI-II) and a second-order formative model of techno-distress among Canadian legal professionals
Bibliographic record
Abstract
Technostress is a phenomenon that needs to be seen as a process rather than a result. This requires the adaptation of measurement tools accordingly. Legal professionals are particularly exposed to technostress. This paper presents the validation of the TSI-II, an updated and bilingual version of the Techno-Stressors Index (TSI). This updated instrument was tested (French-n = 35; English-n = 30) and then retested (Overall-n = 4482; FR-n 1 = 544; ENG-n 2 = 3938) in both languages among Canadian legal professionals. Using the TSI-II, this paper proposes a second-order formative model of techno-distress, including seven techno-stressors, which captures the recent developments associated with the evolution of the technostress literature. Following the best practices for scale development, TSI-II presents excellent properties and is a good predictor of perceived stress among legal professionals. This validation aligns with developments in technostress literature, namely, the conceptual evolution of techno-distress as a component of the technostress process. • TSI-II proposes a second-order formative construct for measuring techno-distress including seven techno-stressors. • The validation process is aligned with the formative evolution of technostress. • The final bilingual instrument was validated in English and in French among Canadian legal professionals. • TSI-II presents excellent properties and is a good predictor of perceived stress among professionals.
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 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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".