Extrinsic and intrinsic determinants of quality of work life
Bibliographic record
Abstract
The objective of this research was to test whether extrinsic, intrinsic or “prior” traits best predict satisfaction with quality of work life (QWL) in health care. Extrinsic traits are salaries and other tangible benefits; intrinsic traits include skill levels, autonomy and challenge. Prior traits are those of the individuals involved, such as their gender or employment status. A survey of employees was conducted in seven different health‐care settings located in the south central region of Ontario, Canada. A total of 65 questions were gathered into scales measuring such factors as co‐worker support, supervisor support and teamwork and communication. These were factor‐analyzed into intrinsic and extrinsic variables, and regressed against a satisfaction scale, with socio‐demographic variables included. Based on the results, the following conclusions can be drawn: objectively identifiable traits of an organization – pay, benefits and supervisor style – play the major role in determining QWL satisfaction. Decision‐makers with an interest in improving QWL in a health‐care institution can focus on these traits and pay correspondingly less attention to enhancing staff autonomy or discretion.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".