The Missing Measure of Loneliness: A Case for Including Neededness in Loneliness Scales
Bibliographic record
Abstract
. More recent scales such as the DLS and SELSA do include items on neededness, but only within their romantic loneliness subscales. This paper proposes that new iterations of loneliness scales should include in all subscales two items on neededness: (a) whether a person feels important to someone else and (b) whether that person has good ways to serve others' well-being. The paper surveys cognate studies that do not rely on loneliness scales but establish a link between neededness and feelings of social connection. It then highlights ways in which neededness items would improve the ability of loneliness scales to specify the risk profile, to delineate variations in the emotional tone and quality of loneliness, and to propose suitable interventions. The paper outlines a theoretical argument-drawing on moral philosophy-that prosociality and being needed are non-contingent, morally urgent human needs, postulating that the protective benefits of neededness vary according to at least four factors: the significance, persistence, non-instrumentality, and non-fungibility of the ways in which a person is needed. Finally, the paper considers implications for the design of appropriate remedies for loneliness.
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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 | 0.050 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| 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 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".