Lessons Learned for Data-Driven Implementation Intentions with Mental Contrasting
Bibliographic record
Abstract
Goal setting and realization are important but challenging. These challenges can be mitigated through effective application of behavior change realization techniques such as implementation intention and mental contrasting (IIMC). IIMC relies on identifying situations compromising desired behavior (i.e., obstacles) and creating action plans to handle those situations (i.e., identifying what, when, and where of actions to prevent or overcome the obstacles). We explore ways historical personal data can enhance the efficacy of IIMC application in the context of improving work-nonwork balance in a probing study with 16 information workers at a large technology company. We share lessons learned from this study that can help designers in further supporting goal realization with data, guide researchers interested in more formal studies of IIMC, and point the research community to important areas of future work on data-driven IIMC, particularly in the work context (e.g., the social dimensions of sense-making and planning).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".