Demandas e recursos de tecnologias de informação e comunicação: evidências de validade de um instrumento
Bibliographic record
Abstract
Contexto: O modelo Demandas-Recursos em Tecnologias de Informação e Comunicação (TIC) propõe que a tecnologia pode atuar como recurso facilitador ou demanda adicional, influenciando o estresse e saúde ocupacional. Objetivo: Adaptar e validar as Escalas sobre Demandas e Recursos de TIC para o contexto brasileiro, explorando suas propriedades psicométricas. Métodos: Participaram 213 trabalhadores brasileiros que utilizavam TIC no desempenho laboral, a maioria do sexo masculino (64,8%) com média de idade de 35,5 anos e formação superior (92,5%). O instrumento foi administrado online, e os dados foram analisados através de Análise Fatorial Confirmatória (AFC) e coeficiente de fidedignidade Ômega. Resultados: A AFC revelou uma estrutura idêntica à original, com oito fatores para a escala de Demandas e dois para a escala de Recursos, ambos com coeficientes Ômega satisfatórios e índices de ajuste adequados. Conclusão: O instrumento apresenta validade psicométrica adequada para investigar demandas e recursos em ambientes de trabalho com TIC, oferecendo uma ferramenta útil para gestores que busquem avaliar e equilibrar esses aspectos no contexto laboral, prevenindo o estresse ocupacional.
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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 | 0.052 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".