The Micropsychosocial Effect of Accounting: A Health Care Context
Bibliographic record
Abstract
This dissertation explores the impact of accounting, identified by qualitative accounting scholars interested in the sociology of accounting as active and pervasive, and able to influence organisations and societies, but at the micro level, by studying the psychology and cognitive processes that facilitate the changes in the individual that eventually lead to the macrosocietal change. It delves deeper into our understanding of accounting’s impact on payment models and pay-for-performance incentives, often challenging the dominant theories used in these areas, such as agency theory. A common concept used when assessing the unintended consequences in accounting is performativity. This dissertation examines two of the four types of performativity in accounting as described by Vosselman (2022): accounting as a general frame/discourse and accounting as an act of calculation. Across three empirical papers and two methodologies (experimental and quantitative archival), this dissertation demonstrates that underlying psychology plays a significant role in the response to an accounting tool or policy, with material effect in the chosen industry of study, healthcare. The first paper demonstrates that policies influenced by economic efficiency, lead to morally ambiguous situations for the doctor. They are unsure as to whether to make decisions about their patient based on the financial frame or the professional one. The first empirical paper finds that the presence of the financial frame, increases the cognitive effort used by doctors, in making a now morally ambiguous decision, and leads to longer patient visit times; the opposite of the desired economic effect. The second paper identifies that fee-for-service payment models disrupt the physician-patient dyad by decreasing the relational aspect of care. This disruption is shown to lead to an increased number of patients visiting the emergency room, thereby creating an external cost driver for emergency room costs. The final paper demonstrates that diverse backgrounds create diversity in motivation. Results show that internationally trained physicians (ITPs) working in Canada do not respond to pay-for-performance incentives in the way that Canadian trained doctors do. The identified effect is directly related to clinical care and decision making. These results show that there is more to learn about accounting’s effect on its environment and the actors in it, and that examination through a psychological lens at the microlevel would be beneficial.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".