Bibliographic record
Abstract
PURPOSE/OBJECTIVES: Although case managers must project professionalism, a dash of healing humor can accomplish a lot of trust in a little space of time. To show how case managers can incorporate humor into case management (CM), the article explores beneficial humor-based interventions and sources of unhealthy humor. Suspending the garment of good humor are 2 main straps: (1) increasing the theoretical knowledge base about healthcare humor for case managers and (2) encouraging knowledge transfer through appropriate humorous exchanges up and down the care continuum bucket brigade. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: With backgrounds in social work, nursing, therapy, and even doctoring, CM practitioners see the soft underbellies of people's lives. From evidence-based research, case managers can garner tips for humor tact and identify ways to incorporate them into CM practice. Recommendations are elaborated to achieve positive outcomes of authentic communication and improving the quality of healthcare experiences. Examples include recognizing boundaries of unfunny and funny, dignifying and humanizing interactions through levity, responding to age groups appropriately, and drawing from client-preference tidbits like inspirational songs and humorous stories. Avoiding negative outcomes is discussed, especially harming with humor. Five common displays of the humor coin's flip side and ethical erosion are presented. FINDINGS/CONCLUSIONS: To aid case managers, caregivers, and clients in fortifying their coping mechanisms, research findings showcase not only the good but also the bad and the ugly such as interventions to avoid. Findings spotlight appropriate uses of humorous antics, bells and whistles signaling low humor and high risk, and simple takeaways case managers can tuck in their satchels. The article's multipronged conclusion is that respectful humor used judiciously can buoy clients' spirits, bring spoonfuls of levity to a sea of seriousness, show humility that softens the stiff authoritarian semblance of control, and increase clients' confidence that their proverbial exposed underbellies are in safe hands. Pile in the little red research vehicle with the author on this purposeful journey of jocularity. As the slogan goes, many true things are said in jest. Hitch up Your Humor Suspenders is one of them.
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 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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".