Using Focus Groups to Modify the Workplace Affective Commitment Multidimensional Questionnaire (Wacmq) for Use in Health Care
Bibliographic record
Abstract
BACKGROUND: The Workplace Affective Commitment Multidimensional Questionnaire (W ACMQ) measures affective commitment towards eight work-related targets. While this questionnaire was developed in the business sector, we believe that the multi-target conceptualization of affective commitment has applicability to complex health care contexts where providers of care, in the production and delivery of care, likely develop commitment toward a multiplicity of targets. Affective commitment is a strong predictor of extra-role workplace behavior; indispensable behaviors which enable health systems to function. OBJECTIVE: The aim of this psychometric exercise is to content validate the WACMQ questions for use in health care. METHODS: Two focus groups were conducted, consisting of nurses working in acute care and emergency hospitals in Ontario. Linguistic validation and cognitive debriefing were used. RESULTS: A total of 14 modifications to the wording of items on the original WACMQ questionnaire were made. CONCLUSIONS: This modified version of the WACMQ reflects the need for researchers in health care settings to acknowledge the complex context of health care and the attendant complexities of worker attitudes. Health care workers can experience affective commitment toward leadership (clinical or administrative), co-workers (nurses or interprofessional), patients, their profession, organization, work or tasks. Further, in some health care settings, features like union membership may have important implications when examining affective commitment or behaviors. Psychometric properties of the modified WACMQ will be established in an upcoming study that will examine the relationships between extra-role behaviors, commitment, perceived organizational support and justice within acute care and emergency departments of hospitals operating in Ontario.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".