P-598 MAPPING TOOLS TO FACILITATE STRATEGIC DECISION-MAKING WHEN RETURNING TO WORK FOLLOWING A DISABILITY
Bibliographic record
Abstract
Abstract Introduction Reaching a shared decision between return to work (RTW) stakeholders is complex when their positions clash and they try to influence decision-making processes. Negotiation then becomes key to a successful RTW. This review aims to map existing negotiation tools. Methods We conducted a mapping review using eight databases (APA PsycInfo, MEDLINE, SocINDEX, ProQuest One Business, Business Source Complete, Scopus, Érudit and Cairn) between July 28 and August 11 2023. The Boolean equation and screening criteria were developed with a librarian based on a negotiation framework. English and French publications were considered. References were recorded in Covidence to be screened by two independent reviewers. To be retained, publication had to: (1) address a negotiation tool (i.e., a means of shaping or influencing decision-making or stakeholders’ attitude); and (2) be applicable in a work or RTW context. Data will be aggregated and summarized in descriptive and operational tables. Results A total of 4,893 references were screened to generate a descriptive table of the publications and a tool inventory. The inventory includes when, by whom, and why to use each tool in RTW negotiation (e.g., preparing for negotiations or influencing decision-making or attitude) and design (e.g., questionnaire, intervention or interview). It will be available for presentation at the conference. Discussion To our knowledge, this is the first study to list useful tools for guiding RTW stakeholder negotiation. Conclusion This study identifies relevant tools and will facilitate adapting them as necessary for a strategic decision-making program to be developed with a RTW coordinator.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".