CHAPTER 12 Behaviours Demonstrating Active Offer: Identification, Measurement, and Determinants1
Bibliographic record
Abstract
In this chapter, we present the behaviours we have identified encouraging and demonstrating the active offer (AO) of social services and health care in French in minority settings, how we measured them, and how we used these measurements to identify the determinants of AO.We will discuss the tools we developed to measure individual AO behaviours, individual perception of organizational support for AO, and personal characteristics that influence AO behaviours (determinants).Various quantitative methods were used to reach our objectives.The results demonstrate that organizational support is the most important determinant of individual AO behaviours.Once controlled for, additional factors included three personal characteristics that increase propensity to demonstrate AO behaviour: education about AO, affirmation of Francophone identity, and competency in French.The sense of competency in English, on the other hand, has a negative association with AO of French-language service.A better understanding of these determinants enables us to refine our strategies to improve the awareness and education of future health and social service professionals on AO.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".